Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents

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Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents (2000)

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by Mitchell A. Potter , Kenneth A. De Jong

Venue:	Evolutionary Computation
Citations:	245 - 5 self

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@ARTICLE{Potter00cooperativecoevolution:,
    author = {Mitchell A. Potter and Kenneth A. De Jong},
    title = {Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents},
    journal = {Evolutionary Computation},
    year = {2000},
    volume = {8},
    pages = {1--29}
}

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Abstract

To successfully apply evolutionary algorithms to the solution of increasingly complex problems, we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. One of the major difficulties is finding computational extensions to our current evolutionary paradigms that will enable such subcomponents to “emerge ” rather than being hand designed. In this paper, we describe an architecture for evolving such subcomponents as a collection of cooperating species. Given a simple stringmatching task, we show that evolutionary pressure to increase the overall fitness of the ecosystem can provide the needed stimulus for the emergence of an appropriate number of interdependent subcomponents that cover multiple niches, evolve to an appropriate level of generality, and adapt as the number and roles of their fellow subcomponents change over time. We then explore these issues within the context of a more complicated domain through a case study involving the evolution of artificial neural networks.

Keyphrases

evolving coadapted subcomponents cooperative coevolution appropriate level complicated domain needed stimulus appropriate number evolutionary pressure major difficulty simple stringmatching task current evolutionary paradigm case study coadapted subcomponents interdependent subcomponents effective technique multiple niche artificial neural network computational extension fellow subcomponents overall fitness evolutionary algorithm complex problem

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