[js1138]

Evolution is an immensely powerful creative process. From the intricate biochemistry of individual cells to the elaborate structure of the human brain, it has produced wonders of unimaginable complexity. Evolution achieves these feats with a few simple processes--mutation, sexual recombination and natural selection--which it iterates for many generations.

Now computer programmers are harnessing software versions of these same processes to achieve machine intelligence. Called genetic programming, this technique has designed computer programs and electronic circuits that perform specified functions.

In the field of electronics, genetic programming has duplicated 15 previously patented inventions, including several that were hailed as seminal in their respective fields when they were first announced. Six of these 15 existing inventions were patented after January 2000 by major research institutions, which indicates that they represent current frontiers of research in domains of scientific and practical importance.

Some of the automatically produced inventions infringe squarely on the exact claims of the previously patented inventions. Others represent new inventions by duplicating the functionality of the earlier device in a novel way. One of these inventions is a clear improvement over its predecessor. Genetic programming has also classified protein sequences and produced human-competitive results in a variety of areas, such as the design of antennas, mathematical algorithms and general-purpose controllers. We have recently filed for a patent for a genetically evolved general-purpose controller that is superior to mathematically derived controllers commonly used in industry. ...continued at the Scientific American Digital

source at www.sciam.com

[tortoise]

For better or worse, genetic programming and other evolutionary systems have limited application in making interesting things happen on standard computers. The problem is that you have to severely constrain the domain or your application will wonder off and do things that have no application to the problem you are actually trying to solve. To make matters worse, standard computational models don't do nearly as well as biological or non-biological chemical systems in being even remotely scalable enough for interesting evolution in a relatively unconstrained domain. The net result is that you can do interesting evolutionary work in chemical systems but not on computer systems, so lacking practical chemical computers genetic programming has been shelved for the most part. In other words, an engineering problem.