Discover Magazine's Darwin Special

Interesting and very instructive words.

"Machine Learning" And "Frontiers In Evolutionary Computation"

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

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

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!!

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

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. :)