Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications

Michael Affenzeller, Stefan Wagner, Stephan Winkler, Andreas Beham

April 16, 2018 by Chapman and Hall/CRC
Reference - 379 Pages - 138 B/W Illustrations
ISBN 9781138114272 - CAT# K35328
Series: Numerical Insights

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  • Describes several generic algorithmic concepts that can be used in any kind of GA or with evolutionary optimization techniques
  • Documents the beneficial effects of enhanced algorithmic concepts, such as relevant alleles preserving genetic algorithm (RAPGA) and self-adaptive segregative genetic algorithm with simulated annealing aspects (SASEGASA)
  • Discusses the application of evolutionary algorithms to benchmark and real-world problems in combinatorial optimization and data-based identification of nonlinear models
  • Provides software, dynamical presentations of representative test runs, and more on the book’s website


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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