Genetic Argonaut

Detecting Groundwater Pollution Source Through Simulated Evolution

2011-08-27T05:00:00.001-03:00

I have not yet seen an application of evolutionary computation like this one: Tracking groundwater pollution to its source. But it seems they also apply other kinds of soft computing (neural networks and simulated annealing).

Excerpt:

They point out that reliable and accurate estimation of unknown groundwater pollution sources remains a challenge because of the uncertainties involved and the lack of adequate observation data in most cases. The non-unique nature of the identification results is also an issue in finding the original source of a pollutant. They have tested the validity of different optimization algorithms including a genetic algorithm, an artificial neural network and simulated annealing and hybrid methods. All of these methods essentially process available data including pollutant concentrations and how these change over time and any monitoring data to home in on a potential source. The benefit of using such algorithms is that as more information becomes available another iteration will take investigators closer to the source.

It seems an interesting application of artificial intelligence.

New Book On The Block

2011-01-31T08:12:00.002-03:00

Jason Brownlee has published a new book on intelligent algorithms and machine learning: Clever Algorithms Nature-Inspired Programming Recipes.

I read the topic on evolution strategies and genetic algorithms it is a well crafted text for quick reading, rapid implementation, code examples and good references at the end of each topic.

You can download the PDF, read it online or buy a real copy!

Professor Hans-Paul Schwefel Will Receive The Frank Rosenblatt Award 2011

2010-11-03T14:44:00.003-03:00

From the press release:

"The 2011 IEEE Frank Rosenblatt Award will be presented at CEC 2011 (www.cec2011.org) to Dr. Hans-Paul Schwefel, Professor Emeritus, Chair of Algorithm Engineering, Faculty of Computer Science, Dortmund University of Technology, Dortmund, Germany for pioneering contributions to evolutionary computation through the theory and application of evolution strategies. Schwefel is a German computer scientist and professor emeritus at Dortmund University of Technology, where he held the chair of systems analysis from 1985 until 2006. He is one of the pioneers in evolutionary computation and one of the authors responsible for the evolution strategies. His work has helped to understand the dynamics of evolutionary algorithms and to put evolutionary computation on formal grounds.

Congratulations, Professor Hans-Paul Schwefel!

Old School Genetic Algorithms News On Google News Archive

2010-10-28T01:06:00.004-03:00

Interesting feature this one from Google News. You can browse old newspapers and read old news.

Looking for genetic algorithms, Google News Archive gives you a nice amount of news dating back to 1980s.

For example, see this simulated annealing news on the excellent magazine Dr. Dobbs -- genetic algorithms and other optimization approaches are cited and/or briefly explained. It's from 1989.

This other one is a brief summary of students and researchers work on engineering mentioning genetic algorithms and the early work of David E. Goldberg during his good times at The Clearing House For Genetic Algorithms. The excerpt:

James Gleick wrote an article on artificial life during its early steps, citing genetic algorithms along the way.

And this another one suggests someone was, as early as 1983, already using genetic algorithms to do stock trading or something related.

Evolving Real Robot Swarms Through Simulated Evolution

2010-10-27T04:14:00.003-03:00

Nice video this one: Deployment of Large Aerial Swarms.

It seems the authors applied an evolutionary algorithm (genetic algorithm?) to do the control of those robots.

Evolving Astronomy Data Mining Through Simulated Evolution

2010-10-27T04:08:00.002-03:00

Interesting stuff this one: The Future of Astronomy is Automated.

It seems genetic algorithms may give another kind of role for traditional astronomers -- and not only for them, but also for other kinds of data miners.

Keep your eyes wide open data miners!

Evolving Car Racing Through Simulated Evolution

2010-09-20T09:50:00.003-03:00

Nice pointer from Martin Pelikan's blog. There is an interesting video of the simulated car racing.

Genetic Algorithms, Complex Systems, Economics. . .

2010-08-28T11:12:00.004-03:00

Interesting on-line paper this one. It shows how the Santa Fé Institute's researchers, genetic algorithms, and complex systems rediscovered a classical way of economics -- but, of course, there is more than just that on the paper. It's worth reading!

Have you ever wondered how free markets could be self-adaptable?

Genetic Algorithms, Proteins, Nuclear Power And More!!!

2010-07-17T10:18:00.004-03:00

A nice summary of a new book on natural computing and, of course, genetic algorithms are a part of it: The Lessons Of Living Things.

Well, it is not a novelty, since computers have been helping us to build things and achieve results since the early beginnings of computing. Surely, the natural approach to computation was a nice move and helped to open a new and wide research field.

Rosenbrock Function And The Steepest Descent

2010-05-05T21:34:00.007-03:00

It's interesting how the steepest descent method sweats a lot if not correctly set up inside the "right" parameters' interval of the Rosenbrock Function:

It's a well known testbed optimization function and evolutionary algorithms can find its optimum with no problems at all -- surely, as long as you try a traditional parameters' setting that any introductory Evolutionary Computation book gives you.

Using the steepest descent and setting it up as:

x = 2

y = 2

Step Size = 0.001

Stop Criterion = 10^-6

I got the following results (see the image below):

Pay attention to the path the steepest descent takes until finding the optimum at x = 1 and y = 1. It is said that an evolution strategy would follow a similar path if its population could be infinite. Since, so far, there are no real computers with infinite memory, such assumption cannot be verified in the real world.

During a second run I set the parameters x = 5 and y = 5. The optimization path run very far away from the previous one! And from the optimum too!

A Bright Future For Soft Computing

2010-04-19T08:58:00.004-03:00

Interesting this article here: Rebirthing HAL.

I think I have been hearing the very same arguments since my first days -- and even earlier -- as a freshman at my former university.

Evolutionary algorithms, neural networks, and others bio-inspired approaches are just profound simplifications of real systems. Don't expect them, at least in the near future, to replicate what their real counterparts do.

Soviet Professor Joins The Timeline Of Evolutionary Computation

2010-04-01T12:58:00.019-03:00

Breaking News

The Soviet evolutionary computation researcher and pioneer, Professor Trollov Buraninev, has been endowed into the Evolutionary Computation Timeline. His pioneer work on the theory of evolutionary algorithms was started much earlier than his Western nemesis' researchers on the same field. A list with some achievements by Professor Buraninev:

Of course, there are many others. The list above is just some of his breakthroughs.

Professor Buraninev states his work is much better and general than "those stuffs made by mainstream German and American researchers! I found a way to optimize any kind of function under any conditions without calculating the covariance matrix or any quasi-Newton-like approach!".

What about Bayesian approaches? Professor Buraninev is categoric: "Bayesian approaches are for n00bs! I like this Western fad of writing words using numbers rather than the letters themselves! I learned it with my would-be girlfriend! If were not for that Californian guy who fooled me, now I would be dating a checkers master girl! Concerning Bayesian approaches, well... In Soviet Russia you can know a posteriori probabilities even without knowing any a priori odds! And vice-versa!"

The director of the American part of the Antarctica special evolutionary computation laboratory, Professor Pangloss Van Thunder-ten-Tronckh, states: "Professor Buraninev's achievement is the direct result of the ice mapping of Antarctica we have been doing since early 1970s. We applied our very special evolutionary algorithm to perform that task. Below, there is the final map we got after some simulation years."

Professor Van Thunder-ten-Tronckh claims that "what you mainstream climate researchers have been argued as global warming is just the work of our team to take some ice from Antarctica and use it in our evening drink parties. The ice sheet shrinking of Antarctica is not more than our continuous 'work' at that continent. There is no global warming! I know this is an inconvenient truth..."

Professor Van Thunder-ten-Tronckh and Buraninev showed us some footage of their Antarctica research place. In the words of Professor Van Thunder-ten-Tronckh: "This footage proves that our laboratory is 100% real! We have been not involved in mainstream evolutionary computation journals because our funding came from CIA and the former KGB and these malevolent agencies forbade us of publishing our work! This picture below shows our supercomputer Metroidska Oblast 5000 proving that P = NP."

"On the faraway foreground, there is our private satellite for cable TV and other 'secret' stuffs, such as the Russian Brides Channel and the Poker Channel."

"This one is very interesting! Our secret satellite is sending us our daily work schedule. Proving that P = NP, making record complexity time algorithms, and answering the Big Question Of The Universe are common tasks we are used to do."

"Here, Professor Buraninev is looking for the aforementioned daily schedule. An uncommon sight, since it is usually done by our grad students and other ~~slaves~~ workers."

"This one shows Professor Buraninev just after having found our daily schedule."

"This one is extremely interesting! It probably shows some Western evolutionary computation researcher trying to take off from us our plans and techniques! I don't know why those guys wear boots that leave strange words printed on snow, such as deg511, HPS, jmerelo and so on!"

"These ones are our ~~slaves~~ grad students crew. They are enjoying their weekly five minutes spare time looking up to the sky -- it's a hobby of theirs! These guys are amazing!"

"The last ones show our main lab site. In this place so many evolutionary computation revolutions took place! It's a pity we have never been allowed to publish our findings... But one day we will be acknowledged as we deserve!"

After all these years, what are the plans of those two pioneers? Let them reply! Professor Pangloss Van Thunder-ten-Tronckh states: "I'm thinking of retiring... I'm tired of doing such a tremendous contributions to evolutionary computation without any sign of acknowledgement! I'll buy a beautiful house at Vladvostok Station or at Nunavut to spend my Summer time there!"

The Soviet evolutionary computation genius, Professor Buraninev, has a different feeling: "The joint evolutionary computation mission made possible to me to have contact with the business friendly ideas from American society. Therefore, I'll invest my money on a start-up bussiness of Russian Brides! Any man will be able to find a Russian bride through evolutionary computation. I'll use all my knowledge to make possible to every man in this planet to marry a Sharapova-like woman!"

The Antarctic evolutionary computation lab is taking a day off to celebrate this date.

An Old School Genetic Algorithm News

2010-03-25T16:09:00.004-03:00

Interesting finding this one! Just click on the picture above and read the news! It's from 1986 and published on Tuscaloosa News. I think it summarizes some aspects of the good times at The Clearing House for Genetic Algorithms. Very old school, indeed.

Five Years Of Genetic Argonaut

2010-03-20T12:25:00.002-03:00

Just before lunch, I reminded this humble blog is achieving its fifth anniversary this day, March 20. A lot of nice stuffs have happened since the beginnings five years ago and I am happy that something that started just as a manner to keep me interested in evolutionary computation could achieve some nice "results", such as the history of evolution strategies post and the publication of a post of mine on the ACM newsletter SIGevolution (Grazie, Pier Luca!).

Along all these years I made some new friends on the blogosphere -- most of them I have never met in person, but I am pretty sure they exist indeed :-) -- and I profound regret any mistake I may have committed to some of them at a given moment.

Despite the blog has been operating on an irregular posting basis, there are nice posts for the coming months. Just stay tuned! :)

Thank you so much for your visits after all these years!

Evolving Wind Turbine Blades Through Simulated Evolution

2010-03-18T15:13:00.004-03:00

Nice video this one:

Evolving Wind Turbine Blades.

Its author applied a genetic algorithm to evolve/optimize the blade shape of a wind turbine. It reminded me of Professor Ingo Rechenberg's work on a similar task: The BERWIAN (Berliner-Windkraft-Anlage) in which his group applied another kind of evolutionary algorithm -- evolution strategies.

The «Berwian» windmill by Ingo Rechenberg takes advantage of the complex eddy effect. Active paddle tips are turned towards the centre, where the turbine is placed. The windmill was optimized by the method of «evolution strategy» at many levels (number and position of the blades, profiles, etc.).

As we are talking about green/clean energy resources, don't miss the chance of reading an interesting post at Martin Pelikan's blog on the same poetical vein (solar panels).

Genetic Argonaut Blog Has Got Its First Publication

2010-01-19T02:21:00.004-03:00

That's it, fellow readers! This blog has got its first publication! :)

This feat owes a lot to our friend Pier Luca Lanzi. Pier Luca kindly invited me (and Professor Schwefel too) to publish the interview I made with Professor Schwefel when evolution strategies celebrated their 45th anniversary.

I am astonished as I had never thought the interview could be published in an official newsletter of such an excellent lineage such as ACM SIGevolution.

If you would like to read the newsletter, see it here. Pier Luca made, as usual, a beautiful work and if I were you I would not miss a single issue of SIGevolution!

Thank you so much, Pier Luca, Professor Schwefel, Professor Eurípedes, and all the SIGevolution guys and gals, for this amazing opportunity!

Genetic Argonaut Has Got Its First Citation

2009-12-13T03:36:00.004-03:00

That's it, my fellow readers! This blog has got its first citation in an academic paper. See the citation here.

The paper is entitled "A comparison of selection, recombination, and mutation parameter importance over a set of fifteen optimization tasks". The paper is in the GECCO 2009 Proceedings

If you would like to cite this blog, feel free to do it!

Thank you so much for the authors who cited Genetic Argonaut!

A Nice Genetic Algorithm Lecture

2009-12-12T04:30:00.004-03:00

Professor David Edward Goldberg (@deg511) delivered a nice lecture on genetic algorithms in which he shares his views on them.

Here you are the lecture in four parts:

Part 1

Part 2

Part 3

Part 4

I have not seen everything yet. As soon as I have done it, I'll share my views on the lecture.

Mega Man + Genetic Algorithm Vs Air Man

2009-12-12T03:55:00.006-03:00

There is a video at YouTube in which the author claims to have developed the fighting style of Megaman using a genetic algorithm to win over Air Man (the boss of the sky-themed level in Mega Man 2).

It is interesting to notice that, over the generations, indeed, Mega Man deals more damage to Air Man. The fitness function is very simple:

F = MMLB - AMLB

MMLB = Mega Man Life Bar
AMLB = Air Man Life Bar

Negative values mean Mega Man failed to win. Otherwise, he was successful.

Just after 10 generations, Mega Man manages to win his first battle and in the next ones his skills are improved.

I wonder if Dr. Light used genetic algorithms to design its robots. If so, he would be a pioneer of evolvable hardware!

Evolving Bugless Programs Through Simulated Evolution

2009-10-26T19:45:00.006-03:00

A brief and nice overview of a genetic programming application for debugging real computer code programs. See it here.

It's almost a surprise to me the programming language used was the good old C: A Real Programming Language Hero!

Ban Genetic Algorithms!!!

2009-09-27T02:29:00.004-03:00

Credit: Arthur Gretton

During the last G-20 Summit in Pittsburgh (USA), some guys from Carnegie Mellon University thought it would be a nice chance to show the whole world how they are concerned about current geopolitical, environmental, and economical problems.

They are not only willing to ban mundane and grievous methods from the evolutionary computation outer space such as genetic algorithms, but they also want to support vector machines, free variables, and start a brigade against Bayesian discrimination! No duality gaps! No greedy search! No power laws! And all the hails for a world in which anyone will freely do a safer data minning!

Evolving Selling Rates Through Simulated Evolution

2009-09-19T09:59:00.004-03:00

There's an interesting article in E-Commerce Times on the use of innovations to improve sales. The author states that artificial intelligence techniques -- such as genetic algorithms, expert systems, and fuzzy systems -- could help in such an endevour. An excerpt:

"Every customer touch point will capture more information that the system can then analyze to increase the chances of making decisions on how to sell more to customers. The software cloud will be using concepts like expert systems, genetic algorithms, and fuzzy logic to make recommendations on how the business will sell more, what to sell where, who to sell to -- and it will modify itself to be able to accomplish these optimizations."

I think big vendors, such as Amazon.com, are already using those AI stuffs.

Genetic Algorithms, Formula 1 Cars, And Speculations...

2009-09-14T18:09:00.003-03:00

An interesting post on the possibility of Formula 1 teams are using genetic algorithms to aerodynamically design their cars.

Another researcher had already done something similar, but completely from scratch! Even though the final result should not be expected to win a championship.

Evolving Brachystochrone Through Simulated Evolution

2009-08-27T14:28:00.010-03:00

In 1696, the famous mathematician of the Bernoulli family, Jean Bernoulli, challenged his peers posing an interesting physical problem:

"Given two points P and Q in a vertical plane and taking into account the straight line holding them is not vertical neither horizontal, what is the curve connecting them such that a particle starting from the higher point P with no initial velocity (V₀ = 0) and sliding down through that line without friction, under the influence of gravity, takes the smallest amount of time to reach the lower point Q?"

A simple and crude schematic of that problem may be seen below:

That problem was presented in the June issue of the famous mathematical journal Acta Eruditorum.

Five other mathematicians replied with a solution and only four of them were published in the next year's May issue of that journal. The whole group was:

The solution curve was named brachistochrone (from the Greek words βραχίστος, brachistos - the shortest; and χρόνος, chronos - time) by Leibniz.

Jean's solution was valid only under certain conditions what made him later propose another and harder version of the same problem, resulting in what became known as calculus of varitations.

This problem itself is an optimization one, in this case optimization of time. So... what about using evolutionary computation to solve the same problem some 320 years later? You may not be of such genius stock as those guys above, but evolutionary computation makes easier to you solve the very same problem! Enjoy it!

The Lizard And The Bird

2009-07-13T14:05:00.002-03:00

No, it is not an ancient tale about human action under the style of a fable, but an invitation to follow at Twitter what happened at GECCO 2009. See this stuff here.

For more GECCO 2009 twittering, see those guys.

Forty Five Years Of Evolution Strategies

2009-06-12T11:30:00.001-03:00

Today, June 12th 2009, is the 45th anniversary of an evolutionary algorithm: Evolution strategies.

So, to celebrate this, somehow, important date to evolutionary computation, I have made a brief interview with Professor Hans-Paul Schwefel concerning what the "unofficial" Arbeitsgruppe Evolutionstechnik had to overcome during the creation of this new optimization procedure. The questions focused on the period before, during, and after the innovation process that brought evolution strategies into life and how he sees the contemporary approaches being used. Here you are the interview.

By the way, I would like to invite you to read the new and much improved post Evolutionary Computation Classics - Volume I, that tells the long version of the story below.

I hope you enjoy it!

01. Could you tell us a little about growing up in post WWII Germany and your high school later years at Canisius College? What kind of student were you? Did your daily life orbit around school-home and home-school?

School went smooth without any problems and without highlights until October 4th, 1957 (launch of the first artificial earth satellite 'Sputnik 1'). From then on, I wanted to become astronaut and became one of the best pupils, especially in maths. I had also lessons in playing the violin, was member of the school orchestra and the school choir, but more important for me were the actions as a boy scout, where I was urged to play a leader's role, very soon - until I needed more time for studying. B.t.w. I needed 90 minutes each per day for the way to and from highschool (class 6 to 13).

02. Why did you choose aerospace technology engineering as your undergraduation degree? Did the brain drain Germany faced (e.g., Operation Paperclip) have any influence on your decision?

Why? - see above! I was not aware of the brain drain (at least I don't remember) and due to my family's financial situation studying away from home was unthinkable.

03. When you were admitted at TUB, during your initial semesters, you had any (at least the vaguest) idea of working with computers and simulation in general?

No, since there were only mechanical calculators operated by means of a rotatable handle, at that time. The first available computer (in the basement of the institute of mechanics) became 'mine' during night times for my diploma thesis' simulation experiments.

04. Before joining in TUB's Hermann Föttinger-Institute for Hydrodynamics (HFI), had you already heard of your evolution strategies fellows (Ingo Rechenberg and Peter Bienert)?

No. I became engaged by invitation from the Head of the institute, because I was his best student (two times best marks in written exams).

05. How did you end up working at TUB's Hermann Föttinger-Institute for Hydrodynamics (HFI)? What was the academic feeling/environment there?

Besides of my duties, i.e. preparing and controlling other students' experiments, I spent much time with Ingo Rechenberg and later also Peter Bienert in performing 'our' work - until all of us were relegated, being said: "Cybernetics as such will no longer be done at this institute".

06. Was the idea of experimental optimization a well known practical method during 1960s engineering Zeitgeist or was it seen as an exotic way of doing research? What did the well established professors at HFI think about that?

The traditional way - as we saw it - was a sequence of creating hypotheses from first principles and known facts plus experiments to prove or falsify the hypotheses. Iteratively improving facilities or processes systematically was rare, though there was some literature about the 'design and analysis of experiments'. Our first successes were highly accepted and even mentioned in the press, but the established experts in the field of hydrodynamics were skeptical or even hostile. Thus we were forced to go back to traditional work or to leave.

07. Can we say the 1960s optimization Zeitgeist was all about linear and non-linear programming? Was gradient-based methods the archetype optimization methods of that time?

Exactly! Optimization methods were a topic of numerical maths. Some opponent said: "We've got the optimal optimization method already..." (he meant steepest descent/ascent) "...and need no more".

08. What were your initial feelings after seeing the results of the experimentum crucis? At least in essence, does that experiment share some features with the modern evolution strategies? To whom the Arbeitsgruppe Evolutionstechnik reported the feedbacks got from that experiment? Could we consider the experimentum crucis the first evolvable hardware experiment ever made?

The first ES version operated with just one offspring per 'generation' because we did not have a population of objects to operate with. I termed it later (1+1)-ES in order to distinguish it from 'multimembered' versions. And we used discrete mutations in the vicinity of the parent's position. In my diploma thesis'(1964/65) work I demonstrated that such more or less local strategy can get stuck prematurely. Therefore I proposed to use probability density distributions like the gaussian one for continuous variables. But we were proud of having shown that the simple ES worked under noisy and (perhaps) multimodal conditions, whereas one-variable-at-a-time and gradient methods failed. And the simple ES was much more efficient (quick) than opponents had predicted. Their (sometimes even now maintained) misconception was to think of uniformly distributed mutations in the
whole search space.

But: There was no proof of convergence nor any theory of the efficiency. Rechenberg's PhD. thesis from 1971 contained the first efficiency results, i.e. the local velocity of the (1+1)-ES in n dimensions of two fitness landscapes.

The first evolvable hardware was created with Peter Bienert's diploma thesis as FORO 1, the first research robot (FOrschungsROboter) able to handle several step motors calibrating e.g. potentiometers at some mechano-electric facility (actually a hybrid computer acting on a pneumatic control device). That was also around 1970.

After the relegation from the institute of hydrodynamics in 1966, I earned my money at industry until 1970 (where I did the nozzle experiments), whereas Ingo Rechenberg and Peter Bienert settled (without salaries) at another institute of the Technical University of Berlin There we met again after I got a grant from the German research foundation for work on comparing ES with other numerical optimization strategies on a digital computer. That money reached from 1970 to 1975 (I finished 'my book' in 1974, which was accepted as dissertation, the defense of which tool part only in 1975). Besides of both theses of ours there were no further publications between 1965 and 1971 since too much time went into writing grant proposals and struggle for survival (except an article of mine in my school's yearbook 1966 and the frequent technical reports for the grant giving authority, i.e. unpublished work).

09. Once I saw a Professor Rechenberg's PPT presentation (only the PPT) in which there was a Galton Box (or bean machine). Was that device used as the mutation operator for the experimentum crucis? Galton boxes may have different distribution/density probability functions (depending on the manner the user set it up), what was the distribution/density function of the box used by the "unofficial" Arbeitsgruppe Evolutionstechnik?

The Galton box was used for demonstration purposes only. Actually, we used some kind of children's playing chips with a plus sign on one side and a minus sign on the opposite side.

In the simplest case with two chips the result could be:
++ with probability 1/4 for changing a variable in positive direction, or;
-- also with probability 1/4 for changing a variable in negative direction, or;
+- (or -+) with probability 1/2 for no change.

With more chips one could of course produce broader binomial probability distributions.

10. What did the judge board think about your senior thesis (undegraduation final project)?

I got the highest mark for that work (I had written the task's requirement myself, which was accepted by the Head of the fluid dynamics institute). Ingo and me got our diplomas at the same time together with a prize from the German Association of Engineers (VDI) for the best 3 students of the year 1965 (Ingo being 6 years, or 12 semesters, older than me).

11. After graduating, you did not join in (right away) a MSc. course or something similar, but went to work repairing Lockheed Super Constellation aircrafts coming back from South America. Didn't you think it was a (somehow) "ungrateful" work compared to all the experimental optimization and computer simulation work you had performed at TUB some few years (months?) earlier?

To put things in right order, my times studying at the TU Berlin required not only at least 10 semesters of topical lectures and exercises with exams, but in addition to that 12 months of practical work (internships), 6 of which had to be completed before entering the first semester, the other 6 in between the following 11 terms, plus studies with exams in 4 non-technical domains like philosophy, laws and economics, arts and foreign languages. It was during the 6 months between the semesters that I joined Lufthansa's dockyards at Hamburg and had to do with Lockheed's SuperConstellations. I also joined a team of specialists concerned with oxygen/hydrogen rocket motors (later on used for Ariane missiles' position control motors) at Boelkow's company near Munich as well as a helicopter jet engines factory (Turboméca) in Southern France.

In Germany there was not such a sharp split between undergraduate and graduate studies. One had to pass a series of exams after about half of the time (the 'Vordiplom') but one could not yet get a job with only that part of the 'basic' studies, normally. For clarity, here is a schedule of mine: 1959 highschool ended, 6 months internship, beginning of studies (after I had in vain tried to get a pilot school place at Lufthansa - the school was closed that year, the only reason why I tried to bridge the gap by joining the university) 1959-1965 studies in aero- and space technology with emphasis on propulsion and further 6 months internships and 'humanistic' studies in between. 1965/1966 coworker at the institute of hydrodynamics (full paid work for studies in turbulent wall shear stress measurement techniques - together with Ingo Rechenberg ((ES things were done aside and not paid for)))

1967-1970 work at industry (AEG research group Berlin, concerned with work on some kind of liquid metal driven energy converter without rotating parts; the flashing nozzle being one essential part of it) 1970-1975 grants from the German research foundation (DFG) for self-defined work, two professors had to serve (better non-serve) as supervisors - a biologist and a control and measurement scientist (Ingo Rechenberg became professor himself by a transition rule in the reorganisation of the university, saying that all people with a high grade Ph.D. exam should be some kind of lower-grade professors; that was around 1973/74 when his dissertation became published as a book).

1976-1985 first 1/2 year on a grant from a research project at Hanover, for which I developed a model of non-genetic variance among cloned guinea pigs; then work in Juelich between Cologne and Aachen in the nuclear research centre (KFA) as systems analyst in a working group doing simulation models of the whole energy system (demand, conversion, consumption with all kinds of energy carriers) in Germany, the European Union and beyond.

1985... professor at a new chair of systems analysis (applied computer science) at the (now also Technical) University of Dortmund - gained for my experience in systems analysis, not evolutionary computation.

12. The two phase flashing nozzle experiment is an interesting example of evolutionary optimization, do you think it would be a practical idea to apply the same approach (or a computer assisted one) to larger nozzles, such as those employed in aerospace industry/research?

One-component one-phase nozzles are theoretically well established and can be designed easily by means of known physical laws. Two-component nozzles (e.g. gas and solid particles in the flow) are a bit more difficult to design due to the shear stresses between fast fluid and slower particles, but there is a lot of empirical knowledge, now.

The problem with 'my' one-component two-phase case suffers additionally from non equilibrium thermodynamics of the phase transition from liquid to a mix of steam (fast) and liquid droplets (slow, also tending to cluster on walls and thus losing their energy content). I think that even now nobody is able to simulate these processes with boiling delay and supersonic shocks in the divergent part of the nozzle in order to optimally design such a flashing nozzle by means of CFD (computational fluid dynamics). But hot water rocket users might make use of my experiences. I tried to correspond with them - they not even answered a line.

13. Basically, what was the core of your work at AEG and KFA Jülich? Were there many opportunities of applying evolutionary computation in their problems?

After my success with the nozzle I was urged to manage a larger project, so that I tried to escape from such non-scientific work as soon as possible. After work I met Ingo Rechenberg and Peter Bienert at their site (control and measurement institute) and hoped for success of my grant application.

14. What was the main subject of your PhD./Dr.-Ing, thesis?

Comparison of Evolution Strategy (ES) with traditional numerical optimization methods, including improvements of ES (e.g. self adaptation of internal parameters, which lead to the later standard (μ, λ) versions), and trying to enhance theoretical analyses.

15. We may consider the years of 1964-1970, at large, as an early developmental and test of concept period for evolution strategies. How would you qualify (for evolution strategies) the subsequent decade, that is, the 1970s? Could we say it was during this decade the self-adaptation mechanism took place?

Exactly. But in 1976 I turned away because such work did not pay, and I became some kind of futurologist at Juelich.

16. After being admitted at Dortmund University as a full professor, what was the main aim your group set up for further research in evolutionary computation and how do you evaluate the results achieved?

I smuggled evolutionry algorithms' ideas into the subchapter 'optimization' in my courses on systems analysis, not forgetting to mention my experiences. It took not long until one and then more and more of my students became interested in just that part of the general topic. For them I tried to get money from research grants, and after 15 years the team had grown up to more than 30 coworkers.

17. Do you consider the genetic algorithm researchers switching from the traditional binary string representation and well known evolutionary operators approach to the estimation of distribution algorithms a (somehow) step toward an ES-like approach, since there is a (somehow) similarity between those algorithms when it comes to probability density/distribution function parameter(s) adaptation?

As always, I argue that any idea improving robustness and/or efficiency (at best: both) of an optimization algorithms is welcome. But, my personal interest (and at least at the beginning also Ingo's) has been in understanding and making use of real nature's tricks, too.

That is why arithmetic tricks not resembling natural processes are of a bit lesser interest to me. Artificial immune systems, ant colony optimization, differential evolution and many other approaches will have their specific domains and a practitioner would be stupid not to have all of them - including traditional methods -in his toolbox.

My experience is that even in one successful run to an optimum it may be necessary to switch between methods and to set some parameters anew by hand, as well.

18. What is your view upon derandomization approaches? Wouldn't be better to keep some "noise" along the evolution optimization process rather than biasing it towards a "direction"?

All of that may be helpful or stupid depending on the specific situation. Noise hampers in 'easy' cases, but helps sometimes in more difficult ones. A good strategy adapts its internal parameters during the search, e.g. the main direction. I call these parameters 'internal model' of the corresponding individuals and found that it is important to maintain diversity of those internal models' within the population. I am dreaming of an evolutionary algorithm that comprises many more internal parameters self-adjusting during the search.

19. Along the history of evolution strategies we can see an addition process of new features (multi-individual population, self-adaptation, variances and covariances, and so on) and these features have became a standard in evolution strategies. When seeing others' approaches, such as genetic algorithms (and the almost forgotten evolutionary programming), we don't verify the same phenomenon, since the most applied genetic algorithm (elitist SGA) are practically the same one established by Kenneth De Jong during the early 1970s, even though there were and there have been well-intentioned works to add new features to genetic algorithm, but they have not became a GA standard. In your opinion, why did that happen in, for example, genetic algorithms' field?

In some cases simple versions are good enough, in some cases people are not even aware of traditional approaches. Sometimes people follow the advice of prophets without reading the bible, and prophets often simplify to spread their message.

The problem of problem solving is multifaceted: First, there is not enough theoretical foundation of the cause/effect relations in optimum seeking methods under various conditions. Second, black box situations (those for which evolutionary algorithms MAY be used) are not classified, perhaps not classifiable. Therefore unpredictability prevails, surprises are common, and convergence to the (a) global optimum within a manageable time cannot be guaranteed. In practice, being better than the competitors is sufficient - for a while.

Thus, even slight improvements towards a (perhaps moving) optimum are appreciated, only theoreticians remain dissatisfied.

20. After forty five years of evolution strategies, what are your impressions for the next forty five?

I cannot forecast. Curiosity prevails - and satisfaction.

Evolving Next Generation Chips Through Simulated Evolution

2009-05-13T17:02:00.003-03:00

See how evolutionary computation is helping to build the next set of microchips. The link:

Engineers Evolve Transistors for Next-Gen Chips.

It seems that as transistors and associated components are getting smaller (nanoscale), some little production process imperfections end up having a considerable performance impact in the final product and one of the desirable aims would be finding a way to, even though those imperfections take place, the final product could deal with them without losing too much performance.

Some English researchers are investigating how to use evolutionary algorithms to cope with that problem.

Genetic Argonaut At Twitter

2009-05-01T09:03:00.003-03:00

I have decided to follow the last Internet fad and create a twitter account for this blog. @geneticargonaut.

Some other evolutionary computation guys are already there and I think I could not miss the party. :)

See you there!

Worse Is Better!

2009-04-20T07:10:00.012-03:00

David Tow has written an instigating article on the role artificial intelligence (AI) methods are playing and will play concerning its use in e-business.

He outlines a set of major methods he believes will become a hotbed for e-commerce and web intelligence:

Evolutionary algorithms
Bayesian networks
Fuzzy logic
Swarm intelligence
Neural networks
Intelligent agents

Except for #4 and #6, I have seen so many enterprise applications of the remaining methods -- being pretty honest, not only enterprise ones, but a highly diversified spectrum of uses of those methods (#1, #2, #3, and #5).

But what David Tow argues is the increasing use of those methods in e-commerce and web intelligence. I think his arguments are well reasoned, since those kinds of endeavours are recent phenomena -- they arose after the Internet and the World Wide Web --, it is normal AI methods have not been widely applied in them so far.

His future trends prognostics are even more instigating:

"The enterprise of the future will increasingly depend on the wide range of rigorous artificial intelligence algorithms and methods outlined above, to drive its operations at all levels of management.

Decision Engineering techniques are at the forefront of this revolution- while IBM has recently set up a new services unit focussed on applying predictive modelling to automate business decisions.

These techniques will continue to be enhanced and packaged in different combinations, to provide immensely powerful problem solving capability as well as integrating with the global intelligence of the Web 4.0.

Major decisions incorporating sophisticated levels of intelligent problem-solving will increasingly be applied autonomously and within real time constraints, in order to achieve the level of adaptability required to survive in an ever-changing and uncertain global environment.

I do not think traditional methods are sentenced to die out, just take a look at the operations research guys that have bred a shining research city on the hill since the early stages of that field around the 1930s and I do not consider they will suddenly vanish from the surface of this planet.

The author did not point an important issue: Better not always means better. The better definition here is not intented to slur other methods, but it embeds what an enthusiast of some research area thinks about his own field/research: Not so rarely, we can see researchers colourfully speaking when it comes to their own research. I consider that a sensible attitude, but hyper-hyped words do not help to solve problems in the real world and the real world, as an old saying states, is a cruel place that may tear into shreds any hype. The chills of the AI Winter can still be felt.

Other important point has to do with the user base a given method owns. Someone may have designed the best time complexity algorithm, the best neural network, the best fuzzy set tunning method, and so on, but if they do not have a significant user base, all those methods are useless -- they may serve as an interesting academic investigation endeavour, but not for application in the real world, since no one would be using them. The ultimate example that illustrates this is the so called worse is better approach:

[...] [S]omething can be "inferior" but still "better". For example, to a particular market or user, software that is limited but exceptionally simple to use may be "better" than software that is more comprehensive but harder to use.

I hope I am not slurring the work of anyone else out there, but the Elitist Simple Genetic Algorithm is, in my humble and insignificant opinion, a perfect example of worse is better in evolutionary computation.

No, I do not consider the elitist SGA a bad method. On the contrary: It is an amazing method devised by Professor John Holland and it has succeeded in so many fields -- thanks to the embedded knowledge its users have included in it and its simplicity. BUT, the elitist SGA, despite its drawbacks, is the most applied genetic algorithm (and evolutionary algorithm) until now, even though there are some new genetic-based techniques that are direct descendants of the SGA, such as EDAs (Estimation of Distribution Algorithms), that were made to overcome some drawbacks inherent to the SGA. Fifteen years after the publication of Baluja's seminal EDA paper, the SGA still is strong, well alive, and bigger than all of its offsprings. Worse is better! :)

Even the SGA siblings (evolution strategies, evolutionary programming and genetic programming) have not got the wide range SGA has. The SGA dominance and its offsprings struggle to obtain a bigger amount in the application market is, in my opinion, an example of worse is better. Would the SGA be an evolutionarily stable strategy? :)

So, I do not believe future problem solving will be strongly tied to artificial intelligence methods. At least not in a fatalistic manner as some believe. Those methods will play a role -- major or minor -- in different problem solving situations, but not to the point of relying 100% on them.

Sounding Stars Through Simulated Evolution

2009-04-16T14:12:00.004-03:00

NASA's Kepler Mission, intended to find Earth-like planets around stars, will use a parallel genetic algorithm to help that task. Read and watch the complete story here.

Per Aspera Ad Astra

2009-03-28T20:33:00.000-03:00

Picture: cortesy of Juan Julián Merelo Guervós

I have learned from Martin Pelikan's blog that Professor Hans-Paul Schwefel will deliver a talk at GECCO 2009 named Failures as Stepping Stones to Success or "Per Aspera Ad Astra". It will be part of a major GECCO 2009 event: Learning From Failures in Evolutionary Computation workshop.

It's such an amazing initiative of organizers to hold a workshop like that, since researchers in general tend only to report their successes and, not so rarely, omit what may be qualified as "failure". How many wonderful ideas have not come from failures? Or, at least, have played the role of a pioneer starting?

Even though I will not be there (what a pity!!!), I can imagine what will be the meat and bones of Professor Schwefel's talk. Sure, I will not tell you, otherwise I would need to kill you... :)

For all those attending GECCO 2009 next summer, I would be very grateful if a good hearted soul could record a video of Professor Schwefel talk and upload it on YouTube or some video sharing service like that. :)

A Video Of A Genetic Algorithm Running On A Cellphone

2009-03-28T20:22:00.002-03:00

My fellow countryman, Christian Perone, has recorded a video of his cellphone genetic algorithm simulation solving a travelling salesman problem.

Really nice stuff! :)

Thank you very much, Christian, for making such an interesting video!

Pervasive Evolutionary Algorithms On Mobile Devices

2009-03-28T20:13:00.004-03:00

So... it is with great happiness that I have learned from our blog friend, Juan Julián Merelo Guervós, about his team work on running evolutionary algorithms on cellphones!

Despite some minor compatibility issues, since cellphones made by the same brand do not communicate properly, the GeNeura Team could achieve interesting results and solved two well known evolutionary computation problems: The travelling salesman problem and the wave function problem.

Congratulations and thank you very much for the post, JJ! I hope you may have even more interesting results on this kind of research! :)

Even More Running Genetic Algorithms On Cellphones

2009-03-23T15:54:00.007-03:00

It seems the work of my fellow countryman, Christian Perone, has inspired minds accross the oceans, reaching the far shores of Australia, where our blog friend, Jason Brownlee, has taken the initiative of running a genetic algorithm on his iPhone.

I think it would be worthful if both of you guys could make a video showing your cellphone screens running the genetic algorithm.

Check Jason's blog out to read the complete and interesting story!

P.S: JJ, we are still waiting some feedback of your cellphone genetic algorithm simulation... :)

Running Genetic Algorithms On A Cellphone

2009-03-21T23:19:00.004-03:00

My fellow countryman, Christian Perone, has made what I had asked some weeks ago: He has run a genetic algorithm on a cellphone!

The genetic algorithm took just 18 generations (8 seconds!) to find the optimum on the sphere function. For more infos about that, check Perone's blog!

If you are a bit curious how he has made it, he has written a how-to upon that. Just read the post.

GA Birthday!!!

2009-03-20T14:12:00.005-03:00

It's Genetic Argonaut fourth birthday!

So many things have happened since the opening of this humble blog on March 20th, 2005, and I have met (mainly on internet) interesting places and persons.

I thank you all outside there on blogosphere for your posts, comments, visits, and so on.

Thank you!

Genetic Algorithm + Travelling Salesman Problem + Playstation PSP = :)

2009-03-20T13:54:00.005-03:00

My fellow countryman Christian Perone -- a gaúcho bred with 100% spiceless real churrasco -- has tried the famous travelling salesman problem on his Playstation PSP genetic algorithm.

He has made a video of his genetic algorithm simulation on that problem. Check it out!

Predicting Collapsing Companies Through Simulated Evolution

2009-03-09T20:45:00.001-03:00

Our blog friend Juan Julián Merelo Guervós has made a post on predicting variables playing major roles on which company is going to close its doors. His group applied genetic programming and self-organizing maps for predicting that.

In an environment full of uncertanties and noisy as financial market and economic-related areas are, it may be an useful tool for all those worried about where their invested money is going after all.

Have our blog friend finally discovered the so feared Doomsday Device? If so, please, reserve my ten females of highly stimulating nature... Since outside it will be nothing to do at all... :)

Будет ласковый дождь

Running A Genetic Algorithm On A Playstation

2009-03-03T18:59:00.004-03:00

My fellow countryman Christian Perone has run, on a Playstation PSP, a genetic algorithm coded in Python.

It's another computer platform conquest genetic algorithms have made. From old school mainframes computers to portable devices, genetic algorithms seem to know no boundaries. What about cell phones? Have genetic algorithms been run on them too? Does someone out there know any story to tell us upon it?

Discover Magazine's Darwin Special

2009-02-15T15:24:00.003-03:00

Interesting and very instructive words.

"Machine Learning" And "Frontiers In Evolutionary Computation"

2009-02-13T17:56:00.010-03:00

I have read a post from our blog friend Julian Togelius concerning key scientific challenges in machine learning. It is from a researcher at Yahoo! It's funny: When someone tries to set up a list of top something (facts, songs, challenges, whatever...) the bias towards the author's own area of interest seems almost inescapable.

It is an extremely limited list of "challenges" and I would not say they are challenges at all. Further I explain why and point to a similar situation I once had while reading an edited evolutionary computation "book". But before begining my arguments, let me tell you that I do not intend, in any way and in any sense, to slur the work/research of anyone else out there. It is just my own opinion and not the real true itself.

Once I read some pages of an evolutionary computation book named "Frontiers Of Evolutionary Computation", an edited volume in which there were some well known researchers stating what they see as a frontier in that field. Despite the grandeur book title when we take a look at the crude reality we realize the most known and applied evolutionary algorithm still is the good old elitist Simple Genetic Algorithm (SGA), which dates back to the early 1970s (or late 1960s). In my opinion, that could be the main frontier in evolutionary computation today: Why, despite all the new types of evolutionary-based algorithms, the most known and used still is the good old elitist SGA? That is ironic. I think the main reason for the elitist SGA's big mainaasuccess is due to the heuristic knowledge its users have embedded in it.

Maybe, the book was not so much about frontiers in evolutionary computation, but research problems the authors were facing and those problem may or may not represent a frontier in that research area -- therefore, I consider a more honest book title would be "Guess What??? We Are Still Using The Good Old Elitist SGA". The same is valid for the Yahoo! guy: I consider that list he made was not composed of key scientific challenges in machine learning, but only key information technology problems Yahoo! has faced. Those problems can be solved through the knowldge science has to give us.

Just a final word about the aforementioned edited book. It seems our time is living an interesting, let's say, post-modern times phenomenon: Edited books. Springer has lots and lots of them, ranging from some well obscure book titles and areas to subjects that hardly will find a passionate reader -- surely, there are nice titles too. Nowadays, anything is eligible to become an edited book: From umbrellas to telephone cabins. I look at those books with a grain of salt: I doubt if, indeed, there is a nice amount of interest on them. For example, take a look at how many persons have bought the book above at Amazon.com.

I hope that some well regarded journals do not endeavour in such a practice. Otherwise, soon we will see journals like "IEEE Transactions On Telephone Cabins".

HollandFest 09

2009-02-13T10:02:00.006-03:00

I was unaware of this event, but the Illigal Blogging guys have brought to my attention the pointer to HollandFest 09. It's an event to celebrate the contributions professor John Holland has made to evolutionary computation, genetic algorithms, complex systems in general and emergence.

Professor David Goldberg has uploaded his presentation at HollandFest 09. It's about the further development of genetic algorithms from the late 1980s until nowadays, remembering some important lessons the lecturer has learnt along the time, giving emphasis to three ones he learnt from his former advisor. He is microblogging about it on Twitter.

So this year already begun so special to evolutionary computation. It's not only Darwin's 200th anniversary; nor 150 years since the publication of his seminal book; nor 45 years from the day two Germans students set up the experimentum crucis that would open one of the branches of evolutionary computation -- Ingo Rechenberg will celebrate his 75th anniversary in this year too!); nor the 20 years since the publication of Goldberg's book about genetic algorithms. But it's also on celebrating John Holland's 80th anniversary and all his contributions to the field he has helped to build.

A Nice Evolutionary Computation Year

2009-02-12T21:02:00.011-03:00

This year, 2009, is full of nice dates to be celebrated!

Today is the birthday of Charles Darwin, the scientist who began evolutionary biology as we know it today. It's Darwin's 200th annivesary.

Also, this year it will be completed 150 years since the publication of a seminal book and one of the most influential along all the human history: On the Origin of Species.

Its contribution to our biological world understanding is tremendous, of course it left for a time so many gaps that Darwin was unable to give the correct and complete answers, but its merits overcast any imperfection it may have.

Another interesting date sends us back to 45 years ago: 1964. The destination is Germany and its beautiful capital city: Berlin. There, a seasoned senior student Ingo Rechenberg and a newbie Hans-Paul Schwefel are about to produce the first results that would pave the way for a branch of evolutionary computation: Evolution Strategies (or Evolutionsstrategie, in German). A crude, simple and -- why not? -- elegant experiment takes place. Its results would show both students the method could be worth to be worked on.

Now, let's jump 25 five years into the future. The year is 1989. An enthusiastic professor from the University of Alabama releases his first book which would set the stage in the near future for so many debates around evolutionary computation, evolutionary algorithms and, of course, genetic algorithms themselves. The book would become an evolutionary computation classic by its own merits, making the fine art of genetic algorithms reachable and, more important, understandable for the large wide audience out there. It was in this book that scientists, practioners, students, and professors had their first contacts with genetic algorithms and evolutionary computation, being hard to find nowadays someone who implemented a genetic algorithm without having heard and/or read the pages of Genetic Algorithms in Search, Optimization, and Machine Learning. Of course, it is impossible to publish something expecting everyone will agree with your ideas and that book has found so many readers along the time having each one a critique view about it. Be the critiques for praise or not, it is difficult not to tell the importance such a book (has) had inside evolutionary computation. But, something is very hard to deny: That is a great book to read. Despite some small imperfections the reader gets what the book promises: A nice introduction to genetic algorithms and enough understanding to code one in computer programming language.

The way Professor David Edward Goldberg teaches the reader is very instructive and clarifying. He even simulates by hand a simple step of a genetic algorithm.

This year is a year of celebration for all of us who had/have a contact with evolutionary ideas. Let's praise and thank all those persons who invested a nice amount of the time of their lives helping to build the fields in which so many researchers, students, and so on have followed since then.

The New Blog On The Block!!

2009-02-10T20:18:00.000-03:00

Another pointer from our Illigal blog friends: Professor Lee Spector has got a blog. He is the editor of Genetic Programming and Evolvable Machines.

It's interesting an editor owning a blog! So, let's welcome him to the evolutionary computation blogosphere.

Evolving Boron Through Simulated Evolution

2009-02-10T20:15:00.000-03:00

A nice pointer from our Illigal Blog friends, see:

Genetic algorithm discovers Boron nearly as hard as diamond.

It reminds us of an earlier post from last year which walks toward the same vein.

Just to warn you: Professor Artem R. Oganov is not the cousin of our favorite Soviet scientist: Professor Trollov Buraninev. :)

Richard Feynman's Affair With Simulated Evolution

2009-01-10T03:46:00.005-03:00

Interesting finding this one. Surely I am not joking, but I should state it is not a profound and complete involvement of Feynman's part -- at least I see through this way and I would not get surprised if I am totally wrong about that. However, as the paper author stated in another article:

The last project that I worked on with Richard was in simulated evolution. I had written a program that simulated the evolution of populations of sexually reproducing creatures over hundreds of thousands of generations. The results were surprising in that the fitness of the population made progress in sudden leaps rather than by the expected steady improvement. The fossil record shows some evidence that real biological evolution might also exhibit such "punctuated equilibrium," so Richard and I decided to look more closely at why it happened. He was feeling ill by that time, so I went out and spent the week with him in Pasadena, and we worked out a model of evolution of finite populations based on the Fokker Planck equations. When I got back to Boston I went to the library and discovered a book by Kimura on the subject, and much to my disappointment, all of our "discoveries" were covered in the first few pages. When I called back and told Richard what I had found, he was elated. "Hey, we got it right!" he said. "Not bad for amateurs."

It would be interesting showing before Feynman's eyes what have been done in the evolutionary computation field since then. His mind is the kind of those the field needs.

Amazing reading for a Saturday afternoon. :)

Even More Mona Lisa Optimization

2008-12-15T03:32:00.005-03:00

Nicolas has made interesting simulations on the same grounds of Mona Lisa's -- he even used the same painting. :)

You may find his post here and an YouTube video here.

Evolving Charles Darwin Through Simulated Evolution

2008-12-12T17:35:00.004-03:00

Another genetic algorithm application similar to Mona Lisa evolution through genetic programming, see it here and here.

The genetic algorithm uses sets of triangles to find the best set fitting Charles Darwin portrait.

Evolving Mona Lisa Through Simulated Evolution

2008-12-09T13:02:00.004-03:00

Amazing story this one! See it here.

It is a genetic programming algorithm designed to evolve Leonardo Da Vinci's Mona Lisa. It seems that each individual is represented by 50 polygons and the fitness function is the real image of Mona Lisa. So, the polygons should be arranged as close as possible to the real one.

The final result is amazing.

Evolving Two-Wheeled Small Vehicles Through Simulated Evolution

2008-12-08T14:50:00.006-03:00

Very interesting pointer this one, see it here.

It is a flash animation that shows you the evolution of two-wheeled small vehicles and their ability to move around a track. Some designs are very strange and deliver no motion at all. Others are extremely similar to real world vechicles, such as motocyles and automobiles, being able, untill a certain extent, to move.

So many thanks to GreyThumb.com guys for the link!

The Vision Of An Evolutionary Computation Pioneer

2008-11-06T13:44:00.028-03:00

Photo By Juan Julián Merelo Guervós

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The post below is a translation from a Spanish posting made by Carlos and put on-line at his blog (La Singularidad Desnuda). See here for the original post.

The text has to do with Professor Hans-Paul Schwefel talk at last EvoStar delivered earlier this year. I should have made the translation much before, but I was unaware of it until yesterday. Despite the delay, Carlos' text is a very good overview concerning what was said during the talk and a valuable one because reports what a person who lived all the development process of an evolutionary algorithm witnessed along that time. I added some date corrections and two pictures I got from Juan Julián Merelo Guervós Flickr album. Thank you for the pictures, JJ!

Thank you very much Carlos for permiting me translating your original text!

I hope you enjoy it!

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One of the best moments during the last week EvoStar event was Professor Hans-Paul Schwefel talk. For an evolutionary computation outsider, it must be said that the three main evolutionary computation branches arose almost simutaneously and in three different places, the algorithms are the following: genetic algorithms (GA); evolutionary programming (EP); and evolution strategies (ES). These last ones were created in Germany during the middle 1960s. Professor Schwefel is one of the creators - together with Professor Ingo Rechenberg (Peter Bienert also contributed with mechanical experiments) - of the first evolution strategy version, the so called two membered elistist evolution strategy or (1 + 1)-ES - later, Professor Schwefel would add more features, such as the self-adaptation mechanism as we know it nowadays and the comma selection scheme. Professor Schwefel is one of the evolutionary computation field pioneers and the talk was named "A Pioneer's View Onto Evolutionary Computation". The talk was very valuable, not exactly by the technical aspects (which were not the main talk focus), but because of the personal perspective Professor Schwefel approached.

Below there is a picture of what the TUB evolution technique working group (Schwefel, Rechenberg, and Bienert) made during the evolution strategies' early years.

Such a talk must be structured through a temporal manner: Past, present, and future. That was the exact talk structure but taking into account an original variation: We begin with the future, going to the present, and finally reaching the past. The two initial parts were very brief. Upon the future, Professor Schwefel showed his hope of what evolutionary computation technology may achieve, however he was sensible not to make accurate predictions. After that, he clarified that part of the talk with some quotes which for some persons may sound embarrassing. The first quote was a comment made by a referee who reviewed Professor Schwefel's seminal evolution strategy work in 1970:

"There is no necessity for another optimization method [except the gradient technique]"

That is an example of a referee whose words are full of glory. The second quote came from an IBM spokesman in 1974:

"Parallel computing will not be available before the year 2000."

That is the way IBM has followed recently. Before the lights of such examples of vision of future, we only must claim that the coming years will have so many surprises concerning the capacity and application of evolutionary algorithms, mainly in hotbed fields facing problems of large complexity, such as biotechnology.

Below we see the cover of Professor Schwefel thesis Adaptive mechanismen in der Biologischen Evolution und ihr Einfluß auf die Evolutiongeschwindigkeit.

Photo By Juan Julián Merelo Guervós

The talk session dealing with the present was very brief too, and it was limited to verifing the exponential growth of the evolutionary computation community and academic production. We enter, then, in the talk part dedicated to the past, where Professor Schwefel reported his experiences in first person since the beginning of evolution strategies, the challenges faced, and all the lessons learned. The first one was "expect the unexpected", and he got it from the experiments made to find the optimal design of a nozzle. That nozzle was conceived as two funnels facing each other: By one of the entrances was injected a fluid composed of gas and a liquid subjected to high velocities, which passed through a small aperture, and was expelled at the other entrance (the nozzle exit). The objective was to achieve the maximum thrust and for that some parameters should be adjusted, such as in which point the small aperture should be put between the two entrances. Professor Schwefel had one of his first "crazy ideas" when thinking that not necessarily the two-funnels design was the optimal design, but there would be two entrances could have another forms of configuration and between them the funnels design could undergo variations, having freedom to vary their forms in three dimmesions. Applying the incipient evolution strategy technology, the following (astonishing) result was got:

The animation shows the evolution of a nozzle design since its initial configuration until the final one. After achieving such a design it was a a little difficult understanding why the surprising design was good and a team of physicists and engineers gathered to provide an investigation aiming at devising some explanation for the final nozzle configuration. Professor Schwefel also investigated the algorithmic features of evolution strategies, what made possible different generalizations such as a surplus of offspring created, the use of non-elitist evolution strategies (the comma selection scheme), and the use of recombination beyond the well known mutation operator to generate the offpsring. The second part of the talk had to do with some topics Professor Schwefel had already approached at past evolutionary computation events, such as the gap between evolutionary computation and natural evolution (static objectives, just one optimization criterion, fixed codification, synchronous evolution, etc.). Among other aspects, Professor Schwefel told about evolution strategies holding spatial structure, using predator-prey models, different gender (male/female) introduction, and diploid codification.

In short, it was an amusement attending such a talk, as much for its content as for the lecturer, a humble and an affable person which is a pleasure to talk with. Talks like that are what makes a conference be remembered along the time.

Evolving UCAV Strategies Through Simulated Evolution

2008-11-05T08:23:00.008-03:00

An article from the Wired Magazine Danger Room section outlines five top national security research challenges for the next president of United States. See it here.

The author says military application of genetic algorithms is an important one when it comes to UCAVs.

"Applications for Genetic Algorithms in Battlefield Operations - This is a natural research progression for an armed forces increasingly willing to conduct operation with unmanned aerial vehicles, or UAVs. Genetic computing and algorithms allow machines to learn through repeated trial and error, as programs can "evolve" to solve extremely difficult artificial intelligence problems. This has very clear applications for battlefield operations. For example, UAVs can be freed to develop the most efficient routes for surveillance, an experiment that has already shown some success. Genetic computing has also shown promise in forecast modeling, and additional research should be conducted to investigate its application to modeling scenarios with national security implications."

It is not a novelty, since military evolutionary computation applications date back to, at least, 1980.

For example, this article from 1991 deals with the optimization of thrust vectoring nozzles using a genetic algorithm. Click here to see the first page. BUT, nozzle optimization dates back to late 1960s and early 1970s, as demonstraded by Professor Hans-Paul Schwefel pioneer work.

Automatic Tuning Of Car Controllers - UPDATED

2008-10-24T17:24:00.006-03:00

Our blog friend and official Viking, Julian Togelius, is asking all the machine learning/artificial intelligence researchers, practioners, enthusiasts, etc. to submit a car controller tuned using your favorite learning algorithm. Further informations may be found here and here. The controllers will be subjected to a competition against each other during a set of races.

If were you, I would submit it immediately! If you don't, then, flee from your hometown or... face the Viking raid!! :)

The first place will be awarded a weekend staying at Valhala, being guided by Valkyries. :)

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I was told, in the comments, by Julian Togelius that there is a video from the last racing car competition they held this year at the last WCCI. See it here.

It is one of the bloodiest races I have ever seen in my whole life!

The blue car is very skilled.

Machine learning researchers of the world, unite! And send your controller to the competition!
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Evolving Proteomics Through Simulated Evolution - UPDATED

2008-10-18T01:12:00.005-03:00

Excellent post by our blog friend Martin Pelikan, see it here.

It deals with the use of a genetic algorithm to select a subset of conformations explaining the experimental scattering profile best.

Despite choosing geophysics as my grad school career, I consider proteomics a big deal and it would worth a lot investing time and dedication pursuing a career in that field.

It would be interesting if Martin told us what kind of genetic algorithm he applied (EDA or classical ones).

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Martin Pelikan explained the evolutionary algorithm they used for evolving protein conformations:

"[...]The genetic algorithm we used is similar to a simple GA, but slightly crossed over with UMDA. We started with this simple method inspired by some prior work in this area and since the method worked, we used it. We tested it on artificially created examples and it worked great, and it gave reasonable results also for the real-world cases. More should be published soon, I hope."

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Thank you very much, Martin! I hope you achieve great results!

PPSN X - A Luso Highlight

2008-09-30T18:09:00.006-03:00

Jorge Tavares has posted his personal summary of PPSN X event. See it here.

He says everything was O.K. along the whole event and praises the PPSN approach concerning poster presentations.

It's interesting: Jorge's summary is the fourth, at least, to point a positive statement of PPSN. So far, PPSN X has received, as long as I am aware of, no negative reviews from the bloggers I know of (JJ, Togelius, and Jorge Tavares). Maybe, it could be a model for others conferences following the evolutionary computation venue.

PPSN X In Pictures

2008-09-25T11:35:00.005-03:00

Our friend, Juan Julián Merelo Guervós, has put on line the nice pictures he took while attending to PPSN X. See his gallery here.

I think the picture above shows Professor Hans-Paul Schwefel's back and the PPSN X attendees are gathering at the backyard of Schwefel's house. (Am I right, JJ?)

Evolving Virtual Creatures Through Simulated Evolution

2008-09-25T11:15:00.004-03:00

Interesting project this one:

"This application is an example of evolutionary computing that you can run on your own Windows PC at home (see the download section). It uses a process similar to biological evolution to gradually evolve a population of virtual creatures in a 3D graphical and physical environment. Much of the inspiration for this project came from the wonderful work of Karl Sims. In the mid 90's Dr. Sims did something very similar, using an evolutionary algorithm to evolve the body plans and control systems of virtual creatures whose bodies were composed of jointed blocks. His creatures were evolved in simulated land and water environments for their ability to swim, walk, jump, follow a light source, and compete against opponents for control of a resource. See the related projects section for links some videos from Dr. Sims and links to other virtual creature evolution projects).

With this program you can watch a process of simulated Darwinian evolution unfold before your eyes (although the process can take several days of computer time depending on your computer speed and your evolution settings). The user is given control of many of the parameters of the evolution such as the size of the creature population, the mutation rate, the ability for which the creatures will be evolved, and many other settings. Users are encouraged to send me any interesting creatures they should happen to produce for inclusion in the Zoo. There are already numerous strange and interesting virtual creatures on display there, with many more to come."

PPSN X - Even More Personal Highlights

2008-09-24T21:13:00.003-03:00

Our blog friend, Juan Julián Merelo Guervós, has written some very nice personal highlights on PPSN X, see them here and here.

He comments on the organization of the event and upon the quality of the papers accepted to be shown at PPSN.

Ride The Lightning

2008-09-24T21:05:00.003-03:00

A lightning struck near my backyard last Friday! It blown up my ADSL modem and my LCD monitor too, I even heard a sound like that one when someone explodes a balloon. It is what I get for living less than 2 degrees below the Equator line. Rain falls almost everyday and lightning follows it.

PPSN X - More Personal Highlights

2008-09-18T09:49:00.004-03:00

Our blog friend, Julian Togelius, who is attending PPSN X, has posted another view of his concerning that conference, see it here.

In this post, Julian draws a critique among the three major evolutionary computation conferences (CEC, GECCO, and PPSN) concerning the kind and quality level of papers sent to them.

For his previous reports, follow this link.

PPSN X - Personal Highlights

2008-09-16T23:51:00.004-03:00

Our blog friend and official viking, Julian Togelius, has posted his first impressions on PPSN X event. See his summaries here and here.

Our another blog friend, Juan Julián Merelo Guervós (call him JJ, for short), organized a singing contest among the attendees and is the official PPSN DJ chair.

PPSN X - Parallel Problem Solving from Nature 10th Edition

2008-09-15T03:45:00.004-03:00

It has already began the 10th edition of the PPSN conference which was started in 1990. See the current edition site here. For a log upon the past editions, see here

It is being held at the Technical University of Dortmund (TUD, former Uni-Dortmund) and you can check the accepted papers here. It seems there will be a wide range of topics on evolutionary computation.

By the way, check the blog entry by our blog friend Juan Julián Merelo Guervós on PPSN X here. Another blog entry here, by Jorge Tavares.

Professor Hans-Paul Schwefel is the PPSN X honorary chair

Let's wait for more blogging from Dortmund at the PPSN X. [Estoy confiando en ti, JJ! Don't disapoint me! :) ]

Evolving Architecture Through Simulated Evolution

2008-09-14T08:11:00.006-03:00

Very good post this one, see it here.

It deals with the optimization of an acoustic shell which delivers the best sound distribution along the space it covers. The author used a genetic algorithm to tune the parameters.

Evolving Fish Swimming Through Simulated Evolution

2008-09-14T07:44:00.004-03:00

Great story about a robot tuna which has its parameters set up by a genetic algorithm. See the link below:

MIT Ocean Engineering - RoboTuna.

It reminds me of an earlier post here:

Evolving Design Through Simulated Evolution.

An excerpt from the robot tuna case:

"The third and final phase is a search for the optimum swimming performance obtainable within the physical limits imposed by the design of the RoboTuna and the length of the existing testing tank. The current analytical intractability of the fluid dynamics of this problem indicated that the most pragmatic way to proceed would be to optimize the body wave controller experimentally. In simple terms, given the seven parameters which control the swimming body wave, this can be thought of as an experimental search through seven dimentional space. This large number of dimensions quickly creates a massive logistics problem (about 282,475,249 combinations of parameters).

Given that it takes approximately 5 minutes to make a single experimental run down the tank, it would take a time frame in the order of millions of years to perform a blind search through all the combinatorial possiblities in the persuit of an optimum (it is no coincidence that this is about the same amount of time it took for the biological tuna to evolve to its present form). Obviously a more efficient search mechanism is needed, in orger to find the optimum before either time ran out or the apparatus failed mechanically. After a survey of many existing multidimensional space search techniques, a robust, seft-optimizing system based on a Genetic Algorithm was developed."

Geophysics, Here I Go!

2008-09-07T17:54:00.000-03:00

Maybe, it is too much early for a definitive decison on which field I should choose. BUT, thinking a little of some key factors, I consider geophysics would be much more interesting to me than a bioinformatics adventure in the unknown.

Being pretty honest, I have curiosity about geophysics, what I have not felt so far when it comes to bioinformatics.

The figure of this post is a phenomenon strongly related to geophysics: An wave. It is The Great Wave off Kanagawa

Day Off - Updated

2008-09-05T01:06:00.006-03:00

Today, I went to the bioinformatics class which I am only attending without having enrolled in it. The professor did not come and gave us no excuses for his absence. I consider the more the professor shows he is committed to the subject, the more he can catch the attention and hearts of students.

I am almost to the point of not going to those classes anymore. I feel it will be fruitless to me and that it would be much more sensible to prepare me to do the admission exam on geophysics.

A little update here: As my blog friend, Juan Julián Merelo Guervós, said in the comments box, the bioinformatics professor's absence has to do with one of the Seven Deadly Sins: Sloth! :)

Well, it is very plausible. :)

The Seven Deadly Sins Of Bioinformatics

2008-09-03T19:50:00.003-03:00

As I am concerned with applying for a bioinformatics MSc., so I am looking for informations of that field, mainly those ones providing a critique panorama of its current (and, of course, future) trends and games. What I have most heard is that bioinformatics is the future, the 21st Century Job, it will help to replace the petrochemical industry bringing to us an alternative energy resource which is not only green, but, also, renewable, and so on. Despite all those positive remarks, I am not interested in them. I want to know critique statements about that field, what is hard to find out when talking to bioinformaticians here, since what they have told makes me feel as though I was in a Marlboro ad. :)

I have found a very interesting presentation about the deadly sins of bioinformatics, see this link:

The Seven Deadly Sins Of Bioinformatics.

The Seven Deadly Sins Of Bioinformatics are the following:

01. Parochialism And Insularity
02. Exceptionalism
03. Autonomy Or Death!
04. Vanity: Pride And Narcissism
05. Monolith Megalomania
06. Scientific Method Sloth
07. Instant Gratification

Gosh! A bioinformatics laboratory being the lair of at least one of those sins could become a hard place to social interaction.

By the way, the presentation is a nice overview of what is happening nowadays in bioinformatics, its current research situation and the views the computer science guys have of the biology guys (and vice-versa).

The Grad School

2008-09-03T01:20:00.001-03:00

That's it!

Next year, I shall be applying for some interesting course in Grad School. Despite the current fad in Brazil concerning the New Wave Of Public Exams to be admitted into one of the three major public Brazilian services (municipality, state, or federal), I would not feel comfortable after five years studying to get my BA. degree on computer/computing engineering to work in something that has little to nothing to do with engineering at all.

I am still a little hesitating upon geophysics or bioinformatics, both of them are amazing fields, but I must take into account the local academic environment and establishment of one and other at my university. The geophysics MSc. is a well established programme and it holds a very good critical mass of geophysicits - in addition, they make big engineering projects (sincerely, those guys are much closer to big engineering than the engineers themselves here).

The bioinformatics MSc. is just in its beginning and, to be honest, I dislike some engineering professors involved in it (for example, once, one of those professors, during a professor meeting, ordered another one to shut his mouth, because the other guy did not have any projects that would bring money to the department, then he could not say his opinion. Veeeeery democratic, isn't it?).

I am aware that I should not "complain" about those engineering professors, but just being completely blind not to see they have little interest in building a local excellence, what the guys from geophysics have already made, and, in my humble opinion, those professors only want to get the money coming from government agencies sponsoring biotech academic projects - it is a new fad happening in Brazil's universities. The professors from the biological sciences center have big bioinformatics ambitions, even telling us that we should seek some way to start up a bioinformatics business of our own. However, when looking at the local bioinformatics business opportunities, we feel anything but unmotivated. The bioscience guys have contributed on interesting genome endevour, such as the gene sequencing of Xylella fastidiosa. I am still thinking if I get into the bioinformatics grad school or not.

One of the key factors that leads me toward geophysics is the fact that in Brazil there are big oil/petroleum companies, such as Petrobras. Bioinformatics is still an activity being mainly sponsored by the Federal Brazilian Government (EMBRAPA, MCT, etc.) and its private ventures are very few. Sure, while it may seem a fruitless bioinformatics field for someone, it could be, by the other hand, a business opportunity before the eyes of another person, a chance to start a business from scratch - I am well aware of those two facets. The main consideration living in my mind these days is that I must make a choice and this choice will affect, likely, my whole life. So, I need to do a sensible choice, what is not easy. There are so many stuffs to ponder.

Early this year, I went to visit the geosciences center and the professor which accepted to be my advisor, as long as I apply for geophysics, treated me in a very very good manner. I liked the way we talked to each other, I felt as though I was his friend since a long time. He invited me to enter his office, to take a sit, and explain my situation. He heard everything, taking heed of each word I said. Few engineering professors treated me like he did.

On the other hand, when visiting the guys responsible for the bioinformatics MSc. programme, I felt like a a being from another planet. The woman who talked to me, told me very inaccurate informations concerning the bioinfo grad programme. Being honest, I disliked the way we interacted. A little more and she would be treading the grounds of impoliteness. I did not get a nice first impression of it.

I feel that I will end up applying for geophysics. BUT, let's see what will happen in the near future and see what field will receive the laurels of evolutionary computation...

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Personal Observations Upon Brazilian Public Service Admission Exams:

It's interesting how lawyers are well paid in Brazilian public services. Often, their salary range from US$ 3000.00 to US$ 6500.00 per month! At Petrobras - one of the most well-paying Brazilian corporations -, an engineer holding just a BA. degree can earn US$ 2150.00 per month! The funniest stuff is that an engineer can agregate knowledge and innovation to a given product, what helps to increase the product's value, while a public service lawyer is only eating away the contributors' money and, at least when it comes to Brazil, they do not deliver a good job, since their profession at service public level is full of corruption, bribery, lobby, ill will, and etc. Of course, there are very good lawyers in Brazil, but the majority is working for the private sector.

That's Brazil!

P.S: I wonder what an American entrepreneur and/or American venture would think about devoting one's life to eat away the contributors money instead of start a new business. My bet: A waste of time and, sure, money! :)

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Estimation of Distribution Algorithms Video Tutorial

2008-08-11T08:44:00.004-03:00

Martin Pelikan has uploaded his nice tutorial upon Estimation of Distribution Algorithms, see it here. The tutorial was given at GECCO 2008.

Even More GECCO 2008 HIghlights

2008-07-23T12:59:00.003-03:00

David Oranchak has made the best GECCO 2008 coverage I have read so far. See it here.

Some excerpts:

According to Nic McPhee’s twitters, GECCO 2008 had 471 attendees from 46 countries. This universal appeal of scientific research is one of things I liked about last year’s GECCO conference, too. This year’s conference, though, was almost as bad as last year’s when it came to feeding the attendees. Food was given out during the two hour poster session where I was presenting my poster, but all the food was gone in less than 20 minutes, and it was not replenished. Boo!

In the defense applications track, we saw a talk about evolving swarm behaviors for unmanned aerial vehicles (UAVs). Some of the swarming, self-organizing, and attacking behaviors are inspired by behaviors of insects such as bees and wasps. Several interesting micro-UAV technologies were mentioned, such as the Black Widow, UAVs with flapping wings (including bat wings), and parasitic (!!!) UAVs such as SilentEyes (it is launched from larger UAVs). Don’t do anything suspicious, or a swarm of these damned things will form a stinging cloud around you.

There were many more interesting papers, too numerous to describe, showcasing the widespread and diverse applications of evolutionary computing. Some examples include evolving circuits with high testability, automatic defect classification in electronic wafer manufacturing, quantifying quality degradation on voice-over-IP networks, detection of malware (including zero-day virus attacks) using techniques inspired by biological immune systems, evolving color schemes for people with color blindness, investment portfolio optimization, modeling the Milky Way galaxy using BOINC volunteer computing, developing no-loss strategies for tic-tac-toe, finding deadlocks in large concurrent java programs, radar jamming, evolving functions that can detect computer program code plagiarism by students (beware, cheaters!), automatic route planning that takes traffic into consideration, automatic composition of rock music using genetic algorithms (seriously?), interactive evolution of facial composites of suspects in criminal investigations, detection of moving objects in videos, using a bacterial foraging algorithm to detect circles on images (wait, what?), evolving a World Computer Chess Champion-beating chess program by mimicking the behavior of a mentor, and prediction of whether a company will have financial losses.

Excellent coverage!!

Evolving Robot Gait Through Simulated Evolution

2008-07-23T04:22:00.003-03:00

Interesting story brought to me via my news webservice:

Students - And Robots - Learn In Professor´s Robotics Lab.

The genetic algorithm applied is the CGA - Cyclic Genetic Algorithm. Not to be confused with the cGA - Compact Genetic Algorithm, an EDA.

The main ideas behind CGA are the following:

Parker made modifications to the standard genetic algorithm to invent the cyclic genetic algorithm (CGA), a method by which cycles of behavior can be learned. The CGA is a method where the computer can self-generate code. In real life, this means that a robot who encounters mud, for instance, might adapt with a different gait. A robot that loses a leg could learn to walk without it.

To demonstrate, Parker changed the parameters on the computer to tell one robot that it was suddenly carrying a heavy load. The robot took on a new walk - slow, deliberate and heavy on stability. In further tests, he showed how the CGA could adapt the robot control codes for partial and full loss of one or two of its legs. "The original CGA method was very limited because it couldn´t react to sensory input," Parker said.

More formaly it can be put as:

"Cyclic Genetic Algorithms were developed to allow for the representation of a cycle of actions in the chromosome. They differ from the standard GA in that the chromosome is in the form of a circle with two tails. The tails of the CGA chromosome are provided to allow for pre and post-cycle procedures. They provide a means for completing tasks before and after entering the cycle. For gait sequence generation, the pre-cycle can position the legs in a ready to walk posture and the post-cycle can return the robot to a stable at rest posture. In our application, we used only the pre-cycle tail. TheCGA genes can be one of several possibilities. They can be as simple as normal genes that represent traits of the individual or they can be as complicated as cyclic sub-chromosomes that can be trained separately by a CGA. For our purposes, the genes represent tasks that are to be completed in a set amount of time. The trained chromosome will contain the cycle of primitive instructions that will be continually repeated by our robot's simple controller to produce a gait.

CGAs can have both fixed and variable length chromosomes. In either case, the system must be able to allot the proper number of tasks to each phase and be flexible enough to allow the CGA to form a complete cycle. When fixed length are used, the tasks at each gene can be repeated. The number of repetitions is encoded in the gene. In this way, fixed length chromosomes can take on the desirable characteristics of variable yet maintain the increased control of training fixed.

More GECCO 2008 Highlights

2008-07-21T13:16:00.002-03:00

Martin Pelikan has posted his presentations at GECCO 2008. See it here.

It's a nice overview on estimation of distribution algorithms showed at GECCO this year.

GECCO 2008 Highlights

2008-07-20T14:21:00.003-03:00

Nice GECCO 2008 overview from Epistasis blog author Jason Moore, see it here.

Darwinmania

2008-07-17T11:31:00.004-03:00

Very interesting articles from New York Times blogger Olivia Judson! In three parts, see them:

Darwinmania!

An Original Confession

Let’s Get Rid of Darwinism

Those articles are intended to celebrate the 150th anniversary of the first announcement of [Darwin's] discovery of natural selection, the main driving force of evolution.

Excellent!!

Evolving Design Through Simulated Evolution

2008-07-04T15:53:00.007-03:00

Amazing article from Elisava:

Bionics And Design: Witnesses To The Evolution Of This Approach.

Some quotes from the text:

"[...] Natural history research, even that which seems to be no more than the fruit of pure and empty curiosity, can have very real uses, which would be enough to justify it even to those who only want research into useful things, if before condemning we could have the patience to wait for time to show the use we could make of its [...]."

Rene-Antoine Ferchault de Reaumur, A History of Wasps - 1719.

"It is the story of the development of the branch of mathematics called the calculus of variations, which concerns questions of optimization —finding forms or patterns that maximize or minimize a particular quantity Is the igloo the optimal housing form for minimal heat loss to the outside? Do bees really use the least possible ammount of wax in constructing their hexagonal cells?"

Stefan Hildebrandt & Anthony Tromba - 1985

"The oldest shells in the universe are the crusts of the cooling stars... We can compare them to an egg-shell: they are formed on the surface of moving liquid drops. In long-ago prehistory, about 400 million years ago, living nature took advantage of the fact that a curved structure is 50 to 100 times stronger than a flat structure of the same thickness. This means that the protecting envelope around fragile micro-organisms can as much reduce the expense of material and weight as obtain a greater degree of protection[...]."

Heinz Isler - 1989

"I believe that flowers —vivacious or woody plants— not only present the most frequent type of shell, but that they are also those of the greatest beauty. They offer a complementary perfection: they are kinetic structures. According to need, they can vary their form to open or close the flower, or even to aid the process of pollinization[...]."

Heinz Isler - 1989

"Nature offers us a range of secrets that will not be revealed except with much patience and love [...]."

Le Ricolais - 1935-1969

Darwin And Wallace - 150 Years

2008-07-01T19:19:00.004-03:00

Nice overview of the joint presentation held in 1858 - 150 years ago - to show up the first skunks upon evolution through natural selection, see here. The main enthusiasts involved in such a discussion were Charles Darwin and Alfred Russel Wallace. Both of them sent a representative to read their respective writings upon the subject. It's funny to know that the main hall of the famous Linnean Society of London was filled with persons holding a layman skill on evolution and science in general, very different from what a contemporary mind would expect, that is, "experts" following the 19th Century biology Zeitgeist.

It is such important day for evolutionary computation, since in 1858 was the "first generation" of its subsequent development!

I wonder what those two men would say if they saw what their ideas - and others' too - has helped to create and solve...

On Penguins And Submarines, Airplanes, And So On!

2008-06-30T18:25:00.007-03:00

Interesting finding here:

Aerodynamics In The Animal World. Yes, I know: Another Google automatic translation. But, it is better to read something slightly comprehensible than nothing at all. The main ideas are very clear, I think. :)

The article has to do with the aerodynamics on animal bodies, such as the Penguin's, and how it deals with aerodynamical problems - turbulence, acceleration issues, and etc.

The Penguin's streamlined body could be an inspiration when it comes to submarine body design, helping to create even quieter, faster, and more efficient submarines. Let alone it could also be applied on other areas strongly relying on aerodynamics, such as airplanes, rockets, and etc.

See a short movie of the Ṕenguin's flight here.

Ingo Rechenberg And Micro Air Vehicles

2008-06-28T12:06:00.003-03:00

A very nice point to read:

Evolution? Innovation!

Yeah! I know... it is an automatic translation from Google. But I think the main ideas are comprehensible.

The current work of Ingo Rechenberg has to with MAVs: Micro Air Vehicles. He is engaged in producing the first functional MAV for real world problems, such as detecting illegal substances and explosives.

So, let's wait for what Der Bioniker will show us on the next years!

Lawrence J. Fogel Highlighted By The American Society For Cybernetics

2008-05-13T12:55:00.005-03:00

Lawrence J. Fogel has been highlighted by the American Society For Cybernetics. See it here. For a complete list of cyberneticians, see here.

Lawrence Fogel is the father of the so called evolutionary programming, which his son, David B. Fogel, later improved and extended to work on real valued domains.

Via Natural Selection, Inc.

On The Analysis Of Biological Mutations

2008-04-11T09:00:00.001-03:00

Browsing MEDAL Blogging, I got a nice link to a post at Olivia Judson's New York Times blog, see it here: A Mutual Affair.

That link was the pointer to another Olivia Judson's post: A Random Analysis. It deals with the biological nature of DNA mutations. Are they really random? Do they follow some kind of probabilistic distribution (or density) function? Are they weighted? Do small mutations happen much more often than the larger ones?

Of course, those questions are mine, not hers. :)

She states that mutations are small modification on the biological blueprint and, depending on the way they happen - small or large -, may affect seriously or not the living being's evolutionary path.

The manner through which mutations occur - be these deleting, inserting, and so on - can also affect the genome's configuration of a specimen. She says that when deletions are more frequent than insertions, the genome's configuration is prone to be compact and small, what may influence the specimen phisiology - those creatures holding small genomes own a heavy metabolic and growing rates.

Mutations are not only random, but also work different from species to species and the mutation flavours (deletion, insertion, etc.) also occur at different rates when taking into account other species. For example, in humans, deletions are more common and prominently working on DNA bases' repetitions, such as ATATAT or AGCAGCAGC. Why do mutations often occur on those repetitions? She explains:

"The reason mutations to repeated sequences are so common is that, in such repeats, it’s easy for the DNA copying machinery of the cell to slip and lose its place, and then put in too many repeats, or too few. (Even for a person, copying something like AAAAAAAA is harder than copying ACTGTCAG. Ahhh!) And although, obviously, these mutations can only happen in part of the genome where there is a repeated sequence, they happen at such a high rate that each of us probably carries as many new slippage mutations as “point mutations” — mutations that swap one base for another, say A for C."

Genomes, as Judson said, hold mutational hotspots and coldspots. These hotspots seem to be long repetitions of DNA bases, since "copying a long repeated segment without slipping is more difficult than copying a short one." Some creatures seem to have evolved their own kinds of mutational hotspots to aim for evolutionary profits - for instance, a pathogen developing the "stealth" ability before the immune system "eyes".

She advises that the manner mutations happen may trap a species in a specific evolutionary path, denying it the exploration of other evolutionary routes:

"But the mutational peppering has a consequence. As I mentioned at the start of this article, an important source of evolutionary novelty is when one member of a pair of duplicated genes evolves to take on a new function. In Neurospora [bread mold] this can’t happen: duplicated gene pairs get destroyed. Its use of mutations to defend its genome from invasion may have inadvertently blocked off some evolutionary paths."

Very nice article! :)

My Very Own Biased Comments

I think that there are so many lessons the evolutionary computation field may learn from Olivia Judson words. Of course, her small and layman article is just an initial step in that direction.

01. On Genetic Coldspots And Hotspots

For example, there is nothing in genetic algorithms that models the so-called mutational hotspots. On the contrary, take a common genetic algorithm book and the author - likely - advises you configuring the mutation rate between the range [0.0001, 0.001]. Crossover rates are set up at some point between [0.6, 0.9]. It seems that macromutations are avoided when it comes to genetic algorithms. When dealing with problems holding strong dependencies among theirs parameters, such a set up may even be harmful for the whole optimization process, since high crossover rates - along small mutation rates - tend to allow gene pool diversity losses, what can stuck the whole population in a specific local optima and/or optimization track. Sure, the GA community has made works on modelling genetic phenomena, such as gene linkage and viral infection.

Of course, detecting and handling the mutational coldspots is very valuable too, since we could avoid flipping bits (or a set of them) that must undergo small modifications or none at all.

I think that the optimization process of a given real world problem could benefit in some sense from that flavour of genetic phenomenon, since mutational hotspots and coldspots seem, at a first glance, to be useful for escaping local optima - remember: some problems require the modification of ALL parameters at same time to escape a local optimum. BUT... they could be harmful too. To avoid that drawback, mechanisms of self-adaptation could help to overtake that type of situation and could even implicitly identify mutational hotspots and coldspots, handling each one according to their respective nature.

Mutational hotspots and coldspots in non-decomposable problems should be addressed considering the relationship between those bits located at those spots and the others - sure, and between those spots' bits themselves. A well designed mutation operator would be very important here. (Again, a self-adaptation mechanism would be helpful.)

Upon the evolution without mutation, see her another nice article: Stop The Mutants!

Another important point has to do with the detection of those mutational hotspots and coldspots. Although there are some works from the Estimation of Distribution Algorithms (EDA) community handling problems that loosely resemble those genetic phenomena (see the Extended Compact Genetic Algorithm or the Hierarchical Bayesian Optimization Algorithm), they were designed under the statistical philosophy of the EDAs - an approach that throws away the genetic stuffs in genetic algorithms replacing them by statistical sampling and probabilistic distribution functions. Let alone that their bioinspired aspect is completely flawed, since in nature there is not an entity that captures data, analyses them, and etc. I guess those methods were designed much more as an optimization tool rather than intended to be a medium to increase our understanding of biological evolution. As optimization tools, those methods have a nice performance, mainly when it comes to discrete nearly decomposable problems. Summing up this paragraph: Despite a loosely resemblance between some genetic phenomena and the inner working of some EDAs, the later were not bioinspired.

For the EDA enthusiasts, I left a simple question: Is there a probability distribution that behaves in the same way mutational hotspots and coldspots do? :)

My hunch: I guess there is!

02. On Gene Deletion And Duplication

One of the few works I am aware of using abstractions of those two genetic phenomena is Professor Hans-Paul Schwefel's nozzle experiment, see it here: Optimization of a Two-Phase Nozzle with an Evolution Strategy. John Koza has used a kind of deletion and duplication too.

He got impressive results applying those genetic phenomena as an experimental optimization abstraction.

Those nozzle designs were obtained through a (1+1)-ES - without computer!

03. On Long DNA Base Repetitions

I have little to say here, since in "01" I have said a lot about a similar phenomenon. But, the way the genes interact in DNA seem to be much more important than their arrangement itself.

In the evolutionary computation realm, that problem has been addressed by linkage learning techniques in genetic algorithms and through correlated mutations in evolution strategies.

EvoWeb Interviews John Koza

2008-04-10T02:50:00.005-03:00

Check his interview here.

I have not been aware that John Koza was John Holland's student along the 1960s.

Interesting interview, despite my own disagreements on some points.

Koza summarizes his path towards genetic programming (GP), the choice for LISP as the main computer language of early GP implementations, the initial papers, the hostility from the GA community at that time (early 1990s), his practitioner style, his achievements, the 1000 nodes computer cluster at Stanford, and so on.

The interview is from 1998.

:.::.::.::.::.::.::.::.::.::.::.::.::.::.:

P.S: There is someone that extremely disagrees upon Koza's statements.

:.::.::.::.::.::.::.::.::.::.::.::.::.::.:

Evolving Movie Contracts Through Simulated Evolution

2008-04-04T12:37:00.005-03:00

MEDAL Blogging has a nice link to an application of genetic algorithms that I have not seen so far: movie contracts between distributors and theaters. See the whole story here.

The author of that investigation, Eunkyu Lee, implemented a hybrid method using genetic algorithm and game theory to help in that kind of business among the parts.

One of the current structures of those contracts is the so-called slide scale:

"These contracts usually come in the form of a sliding scale: in the first week, when the movie is most in demand, the studios get a larger share of the revenue - sometimes as much as 80 percent. As time goes by and demand decreases, the shares even out or reverse, giving theaters a more favorable cut of the revenue. These weekly revenue shares are negotiated on a movie-by-movie basis and are typically augmented by exception clauses for unexpectedly strong box office performances."

The method applied is helping to bring to light new ways to those structures:

"New research by Eunkyu Lee, associate professor of marketing in the Whitman School of Management at Syracuse University, questions the necessity of the complicated and costly contracting practice in the industry. Using a combination of game-theory and an optimization technique called genetic algorithm, Lee and his research partners analyzed different contract structures in simulated movie markets and compared their effectiveness and performance to the current sliding scale structure.

'The major finding of this analysis is that the contract between studios and theaters does not have to be as complicated as the current industry practice,' says Lee. 'Much simpler contracts such as a 50-50 split or a two-part tariff are just as effective as or better than the sliding scale, and they are also not as costly to implement.'"

Remember: "We'll always have Evolutionary Computation." :)

Evolving Genomics Through Simulated Evolution

2008-04-02T21:40:00.004-03:00

Very good pointer from Epistasis blog, see it here.

It outlines the urgent need of computational intelligence approaches in genomics in general and what kind of (tremendously) high dimensional problems that field is facing nowadays. Jason Moore - the article's author - claims computational intelligence can ease those dificulties, softnening, in some sense, the problems' hardnesses.

The section four is of main interest for evolutionary computation fans, since it deals a lot with genetic algorithms, non-linearities in gene interaction, and so on.

Evolutionary Computation Researcher In Antarctica Has Found The Best Time Complexity Evolutionary Algorithm

2008-04-01T06:00:00.001-03:00

My news webservice has brought to me the following evolutionary computation findings:

"An international mission based in Antarctica in a special lab has found the best time complexity evolutionary algorithm ever! Professor Pangloss Van Thunder-ten-Tronckh is the director of the project, which began along the late 1970s and had the former U.S.S.R. as the main manager beside U.S.A - Professor Trollov Buraninev is the director of the U.S.S.R. part. 'It is a finding that will open the doors of a new kind of science among all other sciences', said Professor Pangloss. He continues: 'This discovery is to evolutionary computation as the calculus was to physics and mathematics.'

Professor Pangloss states that his team was the first to solve an over a billion variables problems and they did it using just an AMIGA 500. He comments: 'Although some guys from Urbana-Champaign have claimed that they are the first ones, it is not true! As we are funded by CIA and the former KGB, our results never made the big mainstream publication magazines!'.

He claims he was the inventor of the Estimation of Distribution Algorithms (EDA): 'What outside Antarctica evolutionary computation researchers call EDA, was invented by me when I was an undergrad student during the 1950s! It was a homework that my advisor gave me.'

He also said that what Professor Hans-Georg Beyer has made upon the theory of evolution strategies is, indeed, a less elegant version of what Professor Buraninev's team had already done in 1981. Professor Buraninev states: 'I know very well the work of those German guys! Of course, it is just a lower quality flavour of my own research that I started in April 1st, 1977. What Professor Beyer has made is valid only under certain conditions, that is, Gaussian mutation, intermediate recombination, and comma/truncation selection - let alone that my self-adaptation mechanism is much more general than Schwefel's and/or Rechenberg's, since it can adapt to any kind of fitness/objective function! We made a complete theory of evolution strategies under all imaginable conditions, taking into account correlations, covariances, discrete recombination, noise, plus selection, comma selection, Gaussian mutation, Cauchy mutation, Gamma mutation, Geometric mutation, Bernoulli mutation, Poison mutation, Laplace mutation, uniform mutation, Zipf-Mandelbrot mutation, Student mutation, and so on... both univariate and multivariate distributions! And I did it in my special built Agat computer! And... remember: In Soviet Russia genetic algorithms rotate... YOU!!!'

Professor Pangloss comments about a strange guy that once entered his iglu: 'Yes... he was very strange! He stayed here for almost two weeks and he worn every single day only striped shirts! He promised me that he would teach us how to always win when betting in lottery. I thought him a nice guy and showed him my new invention - Programs that generates, through simulated evolution, others programs! I gave him a source code in LISP! To my very own desperation, years later I was told that he had invented an invention machine and was using my code on it!'

Professor Buraninev once met a Californian guy from La Jolla: 'Yes! That guy once told me that he would introduce me to a friend of his, her name was Blondie24. At a first glance, I thought her name a little strange, since girls do not use to put numbers on their names. But he stated that that was a new Western fad and that the girl was a true master in checkers. Oh, heavens! I would finally realize my Soviet Nerd dream: Date a girl which also is a checkers master! Then, he fixed a day for a meeting. I believed... how fool I was! While the girl, the so-called Blondie24, had not arrived, he showed me some of his father's work on automata. I decided to show him my new evolutionary algorithm flavour which is almost the same as evolution strategies, except on the selection mechanism and the self-adaptation procedure. The guy went insane! He immediately ran away from the Iglu Cafe! Some years later, I got aware that he was engaged in a new business endeavour and using my mind child!! The girl, Blondie24, has never appeared!'

Well, despite all those confusions along the years, Professor Pangloss is really happy to announce the ultimate finding of his lab team. He says: 'Finally, after 30 years of research, we are able to declare that we have found an evolutionary algorithm whose complexity is only O(n^{-100^-100})! This is the complexity of our algorithm in ALL classes of problems! Both decomposable - the easy ones! - and non-decomposable!!' So, where is such a poweful evolutionary algorithm? Professor Pangloss explains: 'Since we have been fooled by those guys from mainstream academia, we will not show how our procedure works! It is even impossible to rescue its inner working, because this was written on a sheet of ice that Professor Buraninev applied on his vodka research!'

But... what about computer codes and simulations? Again, Professor Pangloss has an explanation: 'Our codes, simulations, and computers were all eaten out by a blue whale! This animal is very common by this side of the world and even ate my favourite grad student!'

It seems that evolutionary computation will need to wait a little more to be completely formalized and put on formal grounds!"

Evolving Poetry Through Simulated Evolution

2008-03-29T21:05:00.001-03:00

From Wired and posted at Bruce Sterling's blog, see here.

It is a genetic algorithm that evolves poetry. Each 200 generations a new population of poems is created. There is an interesting video outlining the genetic algorithm general working.

It reminds me of the famous Mark V. Shaney created by our blog friend Don P. Mitchell and put on USENET as a cyber-prank by Rob Pike and Bruce Ellis. See an instance here. I was told by Don Mitchell that his first experiments concerning Mark V. Shaney were performed using the Tao Te Ching text.

An excerpt from the surrealistic Mark V. Shaney composing ability:

"While at a conference a few weeks back, I spent an interesting evening with a grain of salt."

I wonder about the time when those programs really will compose human level literature. What about a Literature Nobel Prize for the next generation evolutionary algorithms?

Professor Schwefel's Keynote At EvoStar

2008-03-27T16:30:00.002-03:00

Our blog friend Juan Julián Merelo Guervós is posting about the EvoStar 2008 event.

His first post is a short summary concerning the opening keynote given by Professor Hans-Paul Schwefel, see it here.

His recent post summarizes a little bit more the keynote presented by Professor Schwefel, see here.

Very interesting posts!

Upon the future of evolutionary computation, Professor Schwefel said that "the unexpected should be expected".

Professor Schwefel also mentioned a situation when a paper reviewer commented the following: "Why should other optimization algorithms be necessary?" This comment is strongly correlated to traditional optimization methods and the development of the evolution strategies, since some researchers - along the 1960s and 1970s - thought that gradient-based methods and others techniques (such as Gauss-Seidel method) were all the stuffs they needed.

He also spoke of the application of evolution strategies (under the form of the so-called experimental optimization) to a two-phase flashing nozzle optimization, which was performed without computers! Even today, it is not possile to calculate what happens within such nozzle: thermodynamically far away from equilibrium, drag between slow water droplets and fast steam, three-dimensional turbulent boundary layer with liquid sublayer, supersonic behind nozzle throat, etc.

There were several challenges to be overtaken when preparing the grounds to the evolution strategy, such as self-adaptation, non-elitist selection method, parallelism, and so on. He obtained inspiration in natural systems to implement solutions to those problems.

An important comment that evolutionary computation researchers should pay attention:

"He's commented that evolutionary algorithms are getting less bio-inspired in time, this is not good or bad, but it's more interesting for him to look at models than to have super-tweaked ultra-tuned purportedly bio-inspired algorithms."

Evolving Crystal Structure Through Simulated Evolution

2008-03-25T10:23:00.008-03:00

From my news webservice I got this interesting sample of genetic algorithm application, see here.

An excerpt:

"[Julio] Facelli, director of the University of Utah's Center for High Performance Computing and a biomedical informatics professor, uses NCSA's Mercury cluster to predict crystal structures for organic molecules that are frequently used in pharmaceuticals, fertilizers, and explosives."

"Modeling the crystal structure of a given substance, Facelli and his team begin with nothing more than the atoms in the molecule and the nature of their bonds. They're looking for structures with the lowest energies, which typically mark the molecules' standard crystal structures or something very close. The problem is that this straightforward data and straightforward goal create billions of possible solutions. Just imagine finding the needle of the lowest energy in that haystack of possible structures."

"'An exhaustive search is not feasible, so we have to have a way to direct the search,' says Facelli. '[Genetic algorithms] are based on the principle of survival of the fittest. Trial solutions compete with one another in the population for survival and produce offspring for the next generation of tests. These algorithms offer excellent scaling properties, which make them good for large-scale parallel computing systems like those at NCSA and emerging computational grids like TeraGrid.'"

Here the article outlines through a simple manner the inner working of the genetic algorithm used:

"For example, an initial calculation on NCSA's Mercury may run 20 simulations on 20 different processors simultaneously, calculating possible crystal structures for a given set of atoms and their bonds. The energies for these structures are compared. The 10 with the lowest energies are kept, and the features of those 10 are mixed and matched to generate the structures for another 10 possible structures. Energies are calculated again, comparisons are made, best candidates are kept, and the cycle continues.

The 'mating operation,' as the mixing and matching is called, stagnates quickly, producing very similar structures over the course of thousands of generations. To combat this lack of variety, the genetic algorithm also introduces arbitrary mutations into the process, occasionally taking one variable from one of the best candidates and including a random number for that variable in the next generation."

Stagnation has been, since immemorial times, a hard drawback to overcome, mainly when using an elitist selection method. Taking into account that those individuals holding the lowest levels of energy are the best ones, then it is, in some sense, normal that the genetic algorithm got stuck quickly. Surely, there already are techniques to treat that, such as truncation selection.

If I were the author and considering he is using mutation and crossover, I would set up a high mutation rate (0.85 to 0.95) and a mild crossover rate (0.45-0.6), of course I would put the elitist rate (the number of fittest individuals saved per generation) as a rate of 1/7 of population size. Inversion could help too!

Swarms

2008-03-20T20:16:00.005-03:00

Very good point from the masterminds behind the Grey Thumb blog. It's a link to Dr. Deborah Gordon's lecture on how ants know to do what they do, see here.

Understanding the inner working of ants' behaviour - and others swarms too - may be important to further the current state of those bio-inspired methods relying on that.

I read her book Ants at Work: How an Insect Society is Organized. She even applied neural networks to model the way that ants interact.

Maybe, the main lesson we may learn from those little insects is directly linked to the fact that ants (and other kinds of swarms) do what they do without a central control and the nest works fine. Sure, when it comes to the evolutionary computation realm, it is always sensible to remember that ants' nests own a very big population and in optimization (and machine learning in general) the population size may be a drawback if set up so big in some problems. Let alone that keeping a small population is very useful when dealing with expensive fitness/objective function evaluations.

Evolving Electrical Circuits Through Simulated Evolution

2008-03-20T19:24:00.006-03:00

Another application of genetic algorithm to optimize circuit components' values, see here.

The resistor's and NTC thermistor's values must be set up such that the resulting curve (from the circuit equation) matches the curve on the chart below:

At a first glance, it seems a very simple problem, but, as the article observes, it may get hard to solve as the circuit complexity increases.

It remembers me of a tale of a senior student in a class of electrical circuits. The professor put some hard circuit problems to be solved through variable substitutions and so on. There were so many derivatives to be solved and etc. The senior student used a tricky way to solve them:

01. He organized all the equations.

02. He used the Laplace transform to all of them.

03. He put all the parameters on a matrix.

04. He solved all the equations through the Gauss-Jordan method.

I was not there, but some "persons" state that the professor refused his solution!!!

I wonder if that professor is the same guy which once said that 1/2 Ohm is not the same as 0.5 Ohm!!!!

Three Years!!!!

2008-03-20T11:30:00.007-03:00

Hi, There!!!!

Today is the third anniversary of this humble and simple blog! :)

I acknowledge the old friends and the new ones too! :)

I hope that you have enjoyed some posts I have made along this time. I have learned a lot writing them.

By the way, it's nice to welcome the new friends, such as Juan Julián Merelo Guervós, Nojhan, Hrafn Thorri Thórisson, Michael Trick, and others!! :)

Thank You! :)

Evolving Grid Computing Optimization Through Simulated Evolution (Updated)

2008-03-01T00:01:00.002-03:00

Interesting stuff I got via my webnews service: Evolving towards the future of science: genetic algorithms and grid computing.

The main objective has to do with the optimization of job scheduling on a computing grid. Through this, lots of valuable resources are saved, such as energy consumption, CPU time, human resources, and so on.

It seems that the genetic algorithm (GA) applied is the good old CGA or Canon Genetic Algorithm (likely holding some slight modifications, for instance elitism, stochastisc tournament selection, etc.), what stands for bit flip mutation, one point crossover and a fitness based selection scheme. Of course, I may be wrong about that, since I have not read nothing (except that news) upon the genetic algorithm they use for that task.

From the article:

"Standing for Distributed Optimal GENEtic algorithm for Grid applications Scheduling, DIOGENES quickly determines the most efficient way to schedule of a set of jobs on a computing grid, optimizing both time and resources in the process."

I have noticed an interesting (strange?) phenomenon which has taken place in the, let's say, New Wave Of Genetic Algorithms (NWOGA): It seems as though no one is applying them! From my surveys inside the Internet and looking for nice real world applications of evolutionary algorithms, what I have noticed is that when it comes to genetic algorithms, the most applied one still is the... Canon Genetic Algorithm! Surely, as stated above, that CGA which is being applied to solve a particular problem, very frequently, holds modifications, be these on the selection scheme, the evolutionary operators (mutation, crossover, and, from time to time, inversion), population size, or even on the probability tunning of mutation and crossover. The fiddling CGA game has generated, sometimes, strange algorithms.

By the way, I have nothing against NWOGA.

Our blog friend, Julian Togelius, wrote an interesting observation upon that situation. I consider that he aimed at the right target, see:

"I think the reason so few people are using other types of evolutionary algorithms than standard GAs is that few people know of, or understand, anything else. It's really amazing how little people know of what's going on next doors to their own little research field.

[...]

So, the people who are busy coming up with new algorithms don't have much time for learning about applications, and vice versa..."

Absolutely! :)

The big academic/technical organizations/publishers (ACM, IEEE, Springer, etc.) could sponsor more crossover events in which different research areas could come a little closer to share their respective experiences, new ideas and so on trying to find common points so that one area could help the other. Perhaps, that attitude could bring more open air to both fields.

P.S: To better understand why I wrote NWOGA, please, see here. :)

Update: Also via MEDAL Blogging.

Feng Gui

2008-02-19T21:47:00.004-03:00

Nice visualization tool for a simulated gradient attention map on a website. See here. Its name is Feng Gui and creates through artificial intelligence techniques a gradient map for the human attention on a picture and/or webpage.

Thanks to François from Intelligent Machines and to MEDAL Blogging for the pointers! :)

The picture above is the Genetic Argonaut's gradient map!

P.S: Well, if the current picture was that one from Lenna, maybe the gradient map could be very different! :)

Evolutionary Computation News

2008-02-19T11:00:00.003-03:00

Interesting news on genetic algorithms and a quick analysis of their academic/design impact upon those (and many other) areas. See here.

As you may see, there are persons which consider genetic algorithms too slow and others think they are too cute. :)

Some excerpts from the news:

"Yet there are drawbacks to the method. Although genetic algorithms have been applied with fantastic success in some cases, they fall short of being universal problem-solvers. And although evolutionary eons can be compressed into hours, finding the precise settings that will give a good solution can take months. How do you figure out which solutions are best? How often do you mutate the offspring? Computer scientists are reduced to trial-and-error knob-twiddling to get the right conditions."

"They’re too cute. Genetic algorithms don’t get bonus credibility just because that’s what nature did,"

"They are quite slow, and they require quite a bit of fiddling,"

"Genetic algorithms’ main contribution today are that they opened the door to biologically-inspired methods. The beauty and success of genetic algorithms motivated other computer scientists to look to biology for inspiration,"

By the way, I consider that genetic algorithms (and evolutionary algorithms in general) are very nice to solve problems, mainly when taking into account the lack of problem informations, such as no derivative available and/or so many constraints to handle.

An Interview With Ingo Rechenberg

2008-01-20T15:00:00.000-03:00

Here you are an interesting interview with Ingo Rechenberg, see here.

The interview concerns his speech upon biological paradigms applied to engineering and technology: The Future Becomes More Biological - A Bionik World in the Year 2099.

There is a video too, see here

His speech was part of the European Futurists Conference 2007.

Leonard A. Rastrigin

2007-12-15T22:03:00.000-03:00

I found the academic website of Leonard A. Rastrigin, see here.

He is the author of the famous Rastrigin Function, see it here. That function is hard to optimize, since it has so many local optima and the global optimum exhibits an inclination/slope. It also is a traditional benchmark in Evolutionary Computation to measure the optimization features of an Evolutionary Algorithm.

The general form of that function is the following:

The 3D shape of it:

As you can see, bigger the value of the steepness, more complex the fitness landscape becomes.

It's a beautiful function!

You can check Rastrigin's publication here.

Evolving Photosynthesis Through Simulated Evolution

2007-11-11T19:36:00.000-03:00

See here how researchers are using evolutionary algorithms to improve the efficiency of photosynthesis process and augmenting the plant productivity without the necessity to apply extra fertilizers.

From the original article:

"Using “evolutionary algorithms,” which mimic evolution by selecting for desirable traits, the model hunted for enzymes that – if increased – would enhance plant productivity. If higher concentrations of an enzyme relative to others improved photosynthetic efficiency, the model used the results of that experiment as a parent for the next generation of tests."

It is the optimization of a process which has been optimized since last 3.4 billion years!

Evolving Missile Trajectory Through Simulated Evolution

2007-10-21T21:48:00.000-03:00

I have found an old school evolution strategy article, from 1980: Optimization of missile trajectories by means of evolution strategy. You can read the first page here.

From the abstract:

Evolution Strategy is an optimum seeking method which attempts to apply the rules of biological evolution - mutation and selection - as closely as possible to technical optimization problems.
In the paper results are presented showing the successful application of evolution strategy to missile trajectory optimization problems like

- Range optimization of a ballistic rocket and e boost-glide missile

- Optimization of miss distance due to lateral wind of a Short range antitank missile

- Optimal trajectory shaping A comparison with other optimization methods is given to show the efficiency of evolution strategy.

----------------

From the introduction we can understand a little of the academic Zeitgeist along that period, see below:

"The paper deals with solutions of optimization problems which we have gained by the application of evolution strategy to missile engineering and development. Evolution strategy is an imitation of nature's way of optimization, a game of mutation and selection. Since nature has been man's teacher in many ways it might appear quite obvious to apply the laws of biological evolution to problems of engineering and technology.

On the Other hand there are also objections to that method, the most important ones being that "cut and try" is a sign of poor engineer and craftsmanship and that we cannot afford nature's lavishness in means and in time to wait for the result. Based on a number of number of encouraging results [1], [2] we have tried to gather our own experience with the matter and will present the results we have achieved.

---------------

It seems that the author worked to the famous German aircraft manufacturer Messerschmitt, which gave birth to the world's first operational turbojet fighter aircraft, the so-called Messerschmitt Me 262.

Below the old Messerschmitt logo.

More On The 2007 IEEE Congress On Evolutionary Computation (CEC)

2007-10-18T11:10:00.000-03:00

Takayuki Higo has a post upon his report on the 2007 IEEE Congress On Evolutionary Computation (CEC), see here.

Don't forget to check the pictures he took!

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The picture above I took from his Flickr personal album.

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2007 IEEE Congress On Evolutionary Computation (CEC)

2007-10-18T00:34:00.000-03:00

Our blog friend, Julian Togelius, has written his own account upon the 2007 IEEE Congress On Evolutionary Computation (CEC), see here.

You can also check some pictures of the congress here.

No complaints on the whole event. It's nice, since the attendee must pay a fee for some basic services - such as, I suppose, internet facilities, lunch place, etc -, what means that the fee was worthwhile. There was even somekind of "safari" through the local zoo!

It seems that the event was O.K.!

ECAL 2007

2007-09-18T22:23:00.000-03:00

Juan Julián Merelo Guervós wrote an interesting post on the ECAL 2007, see here.

There is a blog upon the event, see here.

Don't forget to read the fairytale!