Representations for Genetic and Evolutionary Algorithms

Rothlauf Franz

ISBN 10: 3642880967 ISBN 13: 9783642880964

Editorial: Springer, 2012

Idioma: Ingl�s

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Print on Demand pp. 308 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

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Detalles bibliogr�ficos

T�tulo: Representations for Genetic and Evolutionary Algorithms
Editor: Springer
Fecha de publicaci�n: 2012
Idioma: Ingl�s
ISBN 10: 3642880967
ISBN 13: 9783642880964
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

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.

Rese�a del editor

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.

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