This work was supported by the Santa Fe Institute under its core funding, by National Science Foundation grant IRI-9705830, and by the Biophysics Group at Los Alamos National Laboratory.
Evolutionary computation -- Analysis, Evolutionary programming (Computer science)
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.
Pagie, Ludo, and Melanie Mitchell. "A comparison of evolutionary and coevolutionary search" (2002) Santa Fe Institute Working Paper: 2002-01-002.