Titlebook: Computational Intelligence; A Methodological Int Rudolf Kruse,Christian Borgelt,Pascal Held Textbook 20131st edition Springer-Verlag London

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Fundamental Evolutionary Algorithmsidates, procedures how to select individuals based on their fitness, and genetic operators with which modified solution candidates can be obtained. Equipped with these ingredients we proceed in this chapter to introducing basic forms of evolutionary algorithms, including classical genetic algorithms

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Special Applications and Techniquesnd of metaheuristics. In the first section we consider behavioral simulation for the iterated prisoners dilemma with an evolutionary algorithm. In the next section we study evolutionary algorithms for multi-criteria optimization, especially in the presence of conflicting criteria, which instead of r

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Nonrelativistic Stochastic Quantum Mechanicsn to this field. Among such methods we find artificial neural networks, evolutionary algorithms, and fuzzy systems. Finally, we mention how to use this book and where additional material can be found the Internet.

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S. Bhatnagar,H. Prasad,L. Prashanthin undesired effects can be avoided by adapting the fitness function or the selection method. The last section of this chapter is devoted to genetic operators, which serve as tools to explore the search space, and covers sexual and asexual recombination and other variation techniques.

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