Genetic Algorithms and Genetic Programming: Modern Concepts by Michael Affenzeller, Stephan Winkler, Stefan Wagner, Andreas

By Michael Affenzeller, Stephan Winkler, Stefan Wagner, Andreas Beham

Genetic Algorithms and Genetic Programming: smooth ideas and useful Applications discusses algorithmic advancements within the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to major combinatorial optimization difficulties and describes constitution id utilizing HeuristicLab as a platform for set of rules development.

The e-book specializes in either theoretical and empirical facets. The theoretical sections discover the $64000 and attribute houses of the fundamental GA in addition to major features of the chosen algorithmic extensions built by way of the authors. within the empirical elements of the textual content, the authors practice gasoline to 2 combinatorial optimization difficulties: the touring salesman and capacitated motor vehicle routing difficulties. to focus on the houses of the algorithmic measures within the box of GP, they research GP-based nonlinear constitution identity utilized to time sequence and type difficulties.

Written through center contributors of the HeuristicLab group, this publication offers a greater knowing of the fundamental workflow of fuel and GP, encouraging readers to set up new bionic, problem-independent theoretical suggestions. by means of evaluating the result of average GA and GP implementation with a number of algorithmic extensions, it additionally exhibits find out how to considerably raise conceivable answer quality.

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Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications

Genetic Algorithms and Genetic Programming: glossy thoughts and sensible purposes discusses algorithmic advancements within the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to major combinatorial optimization difficulties and describes constitution identity utilizing HeuristicLab as a platform for set of rules development.

The booklet makes a speciality of either theoretical and empirical elements. The theoretical sections discover the real and attribute homes of the fundamental GA in addition to major features of the chosen algorithmic extensions built by way of the authors. within the empirical elements of the textual content, the authors follow fuel to 2 combinatorial optimization difficulties: the touring salesman and capacitated motor vehicle routing difficulties. to focus on the houses of the algorithmic measures within the box of GP, they examine GP-based nonlinear constitution id utilized to time sequence and type difficulties.

Written via center contributors of the HeuristicLab workforce, this ebook offers a greater knowing of the elemental workflow of gasoline and GP, encouraging readers to set up new bionic, problem-independent theoretical thoughts. through evaluating the result of average GA and GP implementation with numerous algorithmic extensions, it additionally exhibits easy methods to considerably bring up plausible resolution quality.

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Additional resources for Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications

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Representatively, the following books are widely considered very important sources of information about GAs (in chronological order): • J. H. Holland: Adaptation in Natural and Artificial Systems [Hol75] • D. E. Goldberg: Genetic Algorithms in Search, Optimization and Machine Learning [Gol89] • Z. Michalewicz: Genetic Algorithms + Data Structures = Evolution Programs [Mic92] • D. : Evolutionary Computation [DLJD00] The following journals are dedicated to either theory and applications of genetic algorithms or evolutionary computation in general: • IEEE Transactions on Evolutionary Computation (IEEE) • Evolutionary Computation (MIT Press) • Journal of Heuristics (Springer) Moreover, several conference and workshop proceedings include papers related to genetic and evolutionary algorithms and heuristic optimization.

1: Even though expressions are in general not typed in GP, there are cases in which this is not true - a fact which has to be considered during the design and implementation of a GP-based problem solving system. 4 Advantages As we are going to see later, the hierarchical structure tree is not the only way how programs can be modeled and used in the GP process. , their compositions of functions and terminals). In most programming languages, these parse trees are not (conveniently) accessible to the programmer; here we present the programs directly as parse trees as we need to genetically manipulate parts of the programs (sub-trees).

Evolutionary Computation [DLJD00] The following journals are dedicated to either theory and applications of genetic algorithms or evolutionary computation in general: • IEEE Transactions on Evolutionary Computation (IEEE) • Evolutionary Computation (MIT Press) • Journal of Heuristics (Springer) Moreover, several conference and workshop proceedings include papers related to genetic and evolutionary algorithms and heuristic optimization. Some examples are the following ones: • Genetic and Evolutionary Computation Conference (GECCO), a recombination of the International Conference on Genetic Algorithms and the Genetic Programming Conference • Congress on Evolutionary Computation (CEC) • Parallel Problem Solving from Nature (PPSN) Of course there is a lot of GA-related information available on the internet including theoretical background and practical applications, course slides, and source code.

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