Representations for Genetic and Evolutionary Algorithms
Publication year: 2006
ISBN: 978-3-540-32444-7
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The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently.
Subject: Engineering, Evolution, GEA, Genetic and Evolutionary Algorithms, Integer Representations, Operator, Optimal Communication Spanning Tree Problems, Optimization Methods, Theory of Representation, Tree Representations, algorithm, algorithms, calculus, evolutionary algorithm, knowledge, optimization
