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Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications: Numerical Insights

Autor Michael Affenzeller, Stefan Wagner, Stephan Winkler, Andreas Beham
en Limba Engleză Hardback – 9 apr 2009
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.
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Specificații

ISBN-13: 9781584886297
ISBN-10: 1584886293
Pagini: 394
Ilustrații: 138 b/w images, 68 tables and over 100 equations
Dimensiuni: 156 x 234 x 25 mm
Greutate: 0.68 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Numerical Insights


Public țintă

Professional

Cuprins

Introduction. Simulating Evolution: Basics about Genetic Algorithms. Evolving Programs: Genetic Programming. Problems and Success Factors. Preservation of Relevant Building Blocks. SASEGASA—More Than the Sum of All Parts. Analysis of Population Dynamics. Characteristics of Offspring Selection and the RAPGA. Combinatorial Optimization: Route Planning. Evolutionary System Identification. Applications of Genetic Algorithms: Combinatorial Optimization. Data-Based Modeling with Genetic Programming. Conclusion and Outlook. Symbols and Abbreviations. References. Index.

Notă biografică

Michael Affenzeller, Stefan Wagner, Stephan Winkler, Andreas Beham

Descriere

This book describes several generic algorithmic concepts that can be used in any kind of GA or with evolutionary optimization techniques. It 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, the authors show how to substantially increase achievable solution quality. They also describe structure identification using HeuristicLab as a platform for algorithm development. Software, dynamical presentations of representative test runs, and more are available on a supplementary website.