Co-evolving an effective fitness sample: experiments in symbolic regression and distributed robot control

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@InProceedings{DBLP:conf/sac/DolinBR02,
  author =       "Brad Dolin and Forrest H. Bennett III and 
                 Eleanor G. Rieffel",
  title =        "Co-evolving an effective fitness sample: experiments
                 in symbolic regression and distributed robot control",
  booktitle =    "Proceedings of the 2002 ACM Symposium on Applied
                 Computing (SAC)",
  year =         "2002",
  pages =        "553--559",
  address =      "Madrid, Spain",
  month =        mar # " 10-14",
  publisher =    "ACM",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  keywords =     "genetic algorithms, genetic programming, co-evolution,
                 fitness cases, symbolic regression, robot control,
                 distributed control",
  ISBN =         "1-58113-445-2",
  DOI =          "doi:10.1145/508791.508899",
  abstract =     "We investigate two techniques for co-evolving and
                 sampling from a population of fitness cases, and
                 compare these with a random sampling technique. We
                 design three symbolic regression problems on which to
                 test these techniques, and also measure their relative
                 performance on a modular robot control problem. The
                 methods have varying relative performance, but in all
                 of our experiments, at least one of the co-evolutionary
                 methods outperforms the random sampling method by
                 guiding evolution, with substantially fewer fitness
                 evaluations, toward solutions that generalize best on
                 an out-of-sample test set.",
}

Genetic Programming entries for Brad Dolin Forrest Bennett Eleanor G Rieffel

Citations