Most evolutionary algorithms use a fixed representation space. This complicates their application to many problem domains, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This talk discusses a method for specifying algorithms with respect to abstract representations, making them completely independent of any actual representation or problem domain. It also defines a procedure for generating a concrete representation from an explicit characterisation of a problem domain which captures beliefs about its structure. This allows arbitrary algorithms to be applied to arbitrary problems yielding well-specified search strategies suitable for implementation. The process is illustrated by showing how identical algorithms can be applied to both the TSP and real parameter optimisation to yield familiar (but superficially very different) concrete search strategies.
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