Representations for Genetic and Evolutionary Algorithms

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Focusing on the often-overlooked aspect of problem representation in genetic and evolutionary algorithms (GEAs), this monograph explores critical elements that influence GEA performance, including redundancy, scaling, and locality. It applies these theoretical concepts to empirical studies of significant test functions and commercial problems, demonstrating how well-designed representations can enhance efficiency in integer optimization and network design. The findings underscore the importance of representation in achieving success with GEAs.