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Abstract:
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A theoretical framework for understanding the representation issue is presented. This framework is used to explain, as a corollary, conditions under which the genetic algorithm may be expected to eventually get to the absolute maximum of the search space. This framework also explains why utilizing random representations tends to improve performance in genetic algorithm optimization. Numerical simulations illustrating this phenomenon are presented on problems exhibiting complex fitness landscapes.
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