Overview
- A theory of representations is not taken as given, but is developed and applied to real-world problems of commercial importance
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 104)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
About this book
In the field of genetic and evolutionary algorithms (GEAs), much theory and empirical study has been heaped upon operators and test problems, but problem representation has often been taken as given. This monograph breaks with this tradition and studies a number of critical elements of a theory of representations for GEAs and applies them to the empirical study of various important idealized test functions and problems of commercial import. The book considers basic concepts of representations, such as redundancy, scaling and locality and describes how GEAs'performance is influenced. Using the developed theory representations can be analyzed and designed in a theory-guided manner. The theoretical concepts are used as examples for efficiently solving integer optimization problems and network design problems. The results show that proper representations are crucial for GEAs'success.
Authors and Affiliations
Bibliographic Information
Book Title: Representations for Genetic and Evolutionary Algorithms
Authors: Franz Rothlauf
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-642-88094-0
Publisher: Physica Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Physica-Verlag Heidelberg 2002
Softcover ISBN: 978-3-642-88096-4Published: 18 April 2012
eBook ISBN: 978-3-642-88094-0Published: 06 December 2012
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XIV, 290
Topics: Artificial Intelligence, Mathematical and Computational Engineering, Operations Research/Decision Theory, Operations Research, Management Science