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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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",
• URL = "https://www.fxpal.com/publications/co-evolving-an-effective-fitness-sample-experiments-in-symbolic-regression-and-distributed-robot-control.pdf",
• DOI = "doi:10.1145/508791.508899",
• size = "7 pages",
• 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