Four problems for which a computer program evolved by genetic programming is competitive with human performance   [GP]

by

Koza, J., R., Bennett III, F., H., Andre, D. and Keane, M., A.

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Info: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (Conference proceedings), 1996, p. 1-10
Keywords:genetic algorithms, genetic programming
Abstract:
It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a domain-independent automatic programming technique [AP] in which "What You Want Is What You Get" ("WYWIWYG" pronounced "wow-eee-wig"). Genetic programming [GP] is such a technique. This paper surveys three recent examples of problems (from the fields of cellular automata [CA] and molecular biology) in which genetic programming [GP] evolved a computer program that produced results that were slightly better than human performance for the same problem. This paper then discusses the problem of electronic circuit synthesis [CS] in greater detail. It shows how genetic programming [GP] can evolve both the topology of a desired electrical circuit and the sizing (numerical values) for each component in a crossover (woofer and tweeter) filter. Genetic programming [GP] has also evolved the design for a lowpass filter, the design of an amplifier, and the design for an asymmetric bandpass filter that was described as being difficult-to-design in an article in a leading electrical engineering [EE] journal.
Notes:
ICEC-96 Population 640,000 on filter problem, 1% mutation. 64 demes, torrodally connected at end of each generation. 4 emigrant boats of 2% per generation.
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BibTex:
@InProceedings{koza:1996:4problems,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Four problems for which a computer program evolved by
                 genetic programming is competitive with human
                 performance",
  booktitle =    "Proceedings of the 1996 IEEE International Conference
                 on Evolutionary Computation",
  year =         "1996",
  volume =       "1",
  pages =        "1--10",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/icec-96.ps",
  size =         "10 pages",
  abstract =     "It would be desirable if computers could solve
                 problems without the need for a human to write the
                 detailed programmatic steps. That is, it would be
                 desirable to have a domain-independent automatic
                 programming technique in which {"}What You Want Is What
                 You Get{"} ({"}WYWIWYG{"} pronounced
                 {"}wow-eee-wig{"}). Genetic programming is such a
                 technique. This paper surveys three recent examples of
                 problems (from the fields of cellular automata and
                 molecular biology) in which genetic programming evolved
                 a computer program that produced results that were
                 slightly better than human performance for the same
                 problem. This paper then discusses the problem of
                 electronic circuit synthesis in greater detail. It
                 shows how genetic programming can evolve both the
                 topology of a desired electrical circuit and the sizing
                 (numerical values) for each component in a crossover
                 (woofer and tweeter) filter. Genetic programming has
                 also evolved the design for a lowpass filter, the
                 design of an amplifier, and the design for an
                 asymmetric bandpass filter that was described as being
                 difficult-to-design in an article in a leading
                 electrical engineering journal.",
  notes =        "ICEC-96 Population 640,000 on filter problem, 1%
                 mutation. 64 demes, torrodally connected at end of each
                 generation. 4 emigrant boats of 2% per generation.",
}