Detecting Groundwater Pollution Source Through Simulated Evolution

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.

Evolving Astronomy Data Mining Through Simulated Evolution

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

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

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

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.

Evolving Wind Turbine Blades Through Simulated Evolution

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).

A Nice Genetic Algorithm Lecture

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

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

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!

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

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 Next Generation Chips Through Simulated Evolution

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.

GA Birthday!!!

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!

Richard Feynman's Affair With Simulated Evolution

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. :)

Evolving Charles Darwin Through Simulated Evolution

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

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

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!

Evolving UCAV Strategies Through Simulated Evolution

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.

Evolving Proteomics Through Simulated Evolution - UPDATED

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!

Evolving Architecture Through Simulated Evolution

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 Robot Gait Through Simulated Evolution

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.

Evolving Design Through Simulated Evolution

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