ABSTRACT

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 al

chapter Chapter 1|23 pages

Simulating Evolution: Basics about Genetic Algorithms

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chapter Chapter 2|39 pages

Evolving Programs: Genetic Programming

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chapter Chapter 3|4 pages

Problems and Success Factors

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chapter Chapter 4|10 pages

Preservation of Relevant Building Blocks

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chapter Chapter 5|10 pages

SASEGASA – More than the Sum of All Parts

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chapter Chapter 6|8 pages

Analysis of Population Dynamics

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chapter Chapter 7|23 pages

Characteristics of Offspring Selection and the RAPGA

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chapter Chapter 8|35 pages

Combinatorial Optimization: Route Planning

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chapter Chapter 9|50 pages

Evolutionary System Identification

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chapter Chapter 11|86 pages

Data-Based Modeling with Genetic Programming

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chapter |4 pages

Conclusion and Outlook

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