This article proposes a general framework for the use of coevolution to boost the performance of genetic search. It combines coevolution with yet another biologically inspired technique, called lifetime fitness evaluation (LTFE).

Two unrelated problems—neural net learning and constraint satisfaction—are used to illustrate the approach. Both problems use predator-prey interactions to boost the search. In contrast with traditional “single population” genetic algorithms (GAs), two populations constantly interact and coevolve.

However, the same algorithm can also be used with different types of coevolutionary interactions. As an example, the symbiotic coevolution of solutions and genetic representations is shown to provide an elegant solution to the problem of finding a suitable genetic representation.

The approach presented here greatly profits from the partial and continuous nature of LTFE. Noise tolerance is one advantage. Even more important, LTFE is ideally suited to deal with coupled fitness landscapes typical for coevolution.

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