About this book
In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has focused on operators and test problems, while problem representation has often been taken as given. This book breaks away from 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-to-read 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.
- Book Title Representations for Genetic and Evolutionary Algorithms
- DOI https://doi.org/10.1007/3-540-32444-5
- Copyright Information Springer-Verlag Berlin/Heidelberg 2006
- Publisher Name Springer, Berlin, Heidelberg
- eBook Packages Engineering Engineering (R0)
- Hardcover ISBN 978-3-540-25059-3
- Softcover ISBN 978-3-642-06410-4
- eBook ISBN 978-3-540-32444-7
- Edition Number 2
- Number of Pages XVII, 325
- Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
- Additional Information Originally published as volume 104 in the series "Studies in Fuzziness and Soft Computing"
Mathematical and Computational Engineering
Operations Research/Decision Theory
IT in Business
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