Representations for Genetic and Evolutionary Algorithms

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Focusing on the critical role of problem representations in genetic and evolutionary algorithms (GEAs), this book challenges traditional views by exploring how various properties like redundancy, scaling, and locality affect GEA performance. It provides a comprehensive overview of existing knowledge and offers a theory-guided approach for analyzing and designing representations. The text is accessible and aimed at researchers, practitioners, and students, with the second edition enhancing the discussion on basic properties and adding a new chapter on direct representations.