GENETIC ALGORITHMS AND GENETIC PROGRAMMING: MODERN CONCEPTS AND PRACTICAL APPLICATIONS

AFFENZELLER MICHAEL ET.AL

ISBN 10: 1584886293 / ISBN 13: 9781584886297

Edité par Chapman and Hall/CRC, 2009

A propos de cet article

Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide. N° de réf. du vendeur ABNR-5747

Détails bibliographiques

Titre : GENETIC ALGORITHMS AND GENETIC PROGRAMMING: ...

Éditeur : Chapman and Hall/CRC

Date d'édition : 2009

Reliure : Couverture souple

Etat : New

Edition : Edition internationale

A propos de ce titre

Synopsis :

Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for algorithm development.

The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimization problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems.

Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.

À propos de l?auteur:

Michael Affenzeller, Stefan Wagner, Stephan Winkler, Andreas Beham