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

  • Authors:

  • Brad Dolin;Forrest H Bennett, III;Eleanor G. Rieffel

  • Affiliations:

  • Stanford University, Stanford, CA;Pharmix Corporation, Redwood Shores, CA;FX Palo Alto Laboratory, Palo Alto, CA

  • Venue:
  • Proceedings of the 2002 ACM symposium on Applied computing
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

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.