Representations for Genetic and Evolutionary Algorithms

Front Cover
Springer Science & Business Media, 14. märts 2006 - 325 pages

In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has been focused on operators and test problems, while problem representation has often been taken as given. This book breaks with this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance. 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. The book is written in an easy-readable style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.

 

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Contents

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Copyright

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Page 313 - In JJ Grefenstette (Ed.), Proceedings of the Second International Conference on Genetic Algorithms (pp.
Page 319 - A survey [17] of optimization by building and using probabilistic models. IlliGAL Report No. 99018, University of Illinois at Urbana-Champaign. Illinois Genetic Algorithms Laboratory, Urbana.
Page 325 - Thierens, D. (1995). Analysis and design of genetic algorithms. Leuven, Belgium: Katholieke Universiteit Leuven. Thierens, D. and DE Goldberg (1994). Convergence models of genetic algorithm selection schemes.
Page 313 - Raidl (1999). Characterizing locality in decoder-based eas for the multidimensional knapsack problem. In C. Fonlupt, J.-K. Hao, E. Lutton, E. Ronald, and M. Schoenauer (Eds.), Proceedings of Artificial Evolution, Volume 1829 of Lecture Notes in Computer Science, pp.

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