The Vision Of An Evolutionary Computation Pioneer

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

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.