Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications

ISBN-13: 9781584886297 / Angielski / Twarda / 2009 / 394 str.

Michael Affenzeller;Stefan Wagner;Stephan Winkler

Opis

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.

Genetic algorithms and genetics programming are known to achieve robust, high-quality solutions to difficult problems. Due to increasing computing power, these methods have been successfully applied to problems in logistics, data mining, and various other fields with complex data. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications introduces basic concepts in an intuitive way. Designed as both a reference and self-study guide for readers from different backgrounds, the book includes theoretical material as well as practical applications. It also provides a supplementary web page with software and source code for heuristic laboratory work, along with illustrative examples.

Spis treści

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.

O autorze

Michael Affenzeller, Stefan Wagner, Stephan Winkler, Andreas Beham