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Memetic algorithms (MAs) are evolutionary algorithms (EAs) that apply a separate local search process to refine individuals (e.g.
improve their fitness by hillclimbing). These methods are inspired by models of adaptation in natural systems that combine
evolutionary adaptation of populations of individuals with individual learning within a lifetime. Additionally, MAs are inspired by
Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement. Thus a
memetic model of adaptation exhibits the plasticity of individuals that a strictly genetic model fails to capture by allowing the co-evolution
of both genes and memes. Under different contexts and situations, MAs are also known as hybrid EAs, genetic local searchers, Baldwinian EAs, Lamarkian EAs, etc.

This Memetic Algorithms Discussion List is a forum for interaction among scholars
about the exciting field of Memetic Algorithms.

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