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Representations for Genetic and Evolutionary Algorithms
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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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Springer-Verlag Berlin and Heidelberg GmbH & Co. KG; March 2006
338 pages; ISBN 9783540324447
Read online, or download in secure PDF format
338 pages; ISBN 9783540324447
Read online, or download in secure PDF format
Subject categories
- Academic > Mathematics > General > Mathematics
- Academic > Computer Science > Computer science
- Academic > Computer Science > Electronic data processing
- Academic > Computer Science > Computer programming
- Academic > Computer Science > Functional programming
- Academic > Computer Science > Genetic programming
- Academic > Mathematics > Instruments and machines
- Academic > Mathematics > Geometry. Trigonometry.Topology
- Mathematics > Applied
- Mathematics > Algebra
- Computers > Artificial Intelligence
ISBNs
3540324445
9783540250593
9783540324447