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A Comparison of Evolutionary and Coevolutionary Search

Author

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  • Ludo Pagie
  • Melanie Mitchell

Abstract

Previous work on coevolutionary search has demonstrated both successful and unsuccessful applications. As a step in explaining what factors lead to success or failure, we present a comparative study of an evolutionary and a coevolutionary search model. In the latter model, strategies for solving a problem coevolve with training cases. We find that the coevolutionary model has a relatively large efficacy: 86 out of 100 (86%) of the simulations produce high quality strategies. In contrast, the evolutionary model has a very low efficacy: a high quality strategy is found in only two out of 100 runs (2%). We show that the increased efficacy in the coevolutionary model results from the direct exploitation of low quality strategies by the population of training cases. We also present evidence that the generality of the high-quality strategies can suffer as a result of this same exploitation.

Suggested Citation

  • Ludo Pagie & Melanie Mitchell, 2002. "A Comparison of Evolutionary and Coevolutionary Search," Working Papers 02-01-002, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:02-01-002
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    Cited by:

    1. Cerruti, Umberto & Dutto, Simone & Murru, Nadir, 2020. "A symbiosis between cellular automata and genetic algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).

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