Abstract
The fundamental distinction between ordinary evolutionary algorithms (EA) and co-evolutionary algorithms lies in the interaction between coevolving entities. We believe that this property is essentially game-theoretic in nature. Using game theory, we describe extensions that allow familiar mixing-matrix and Markov-chain models of EAs to address coevolutionary algorithm dynamics. We then employ concepts from evolutionary game theory to examine design aspects of conventional coevolutionary algorithms that are poorly understood.
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Ficici, S.G., Pollack, J.B. (2000). A Game-Theoretic Approach to the Simple Coevolutionary Algorithm. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_46
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DOI: https://doi.org/10.1007/3-540-45356-3_46
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