Articles liés à Foundations of Global Genetic Optimization
Synopsis
Genetic algorithms today constitute a family of e?ective global optimization methods used to solve di?cult real-life problems which arise in science and technology. Despite their computational complexity, they have the ability to explore huge data sets and allow us to study exceptionally problematic cases in which the objective functions are irregular and multimodal, and where information about the extrema location is unobtainable in other ways. Theybelongtotheclassofiterativestochasticoptimizationstrategiesthat, during each step, produce and evaluate the set of admissible points from the search domain, called the random sample or population. As opposed to the Monte Carlo strategies, in which the population is sampled according to the uniform probability distribution over the search domain, genetic algorithms modify the probability distribution at each step. Mechanisms which adopt sampling probability distribution are transposed from biology. They are based mainly on genetic code mutation and crossover, as well as on selection among living individuals. Such mechanisms have been testedbysolvingmultimodalproblemsinnature, whichiscon?rmedinpart- ular by the many species of animals and plants that are well ?tted to di?erent ecological niches. They direct the search process, making it more e?ective than a completely random one (search with a uniform sampling distribution). Moreover, well-tunedgenetic-basedoperationsdonotdecreasetheexploration ability of the whole admissible set, which is vital in the global optimization process. The features described above allow us to regard genetic algorithms as a new class of arti?cial intelligence methods which introduce heuristics, well tested in other ?elds, to the classical scheme of stochastic global search.
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Foundations of Global Genetic Optimization
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Foundations of Global Genetic Optimization (Studies in Computational Intelligence, 74)
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Foundations of Global Genetic Optimization (Hardcover)
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Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2007
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Hardcover. Etat : new. Hardcover. Genetic algorithms today constitute a family of e?ective global optimization methods used to solve di?cult real-life problems which arise in science and technology. Despite their computational complexity, they have the ability to explore huge data sets and allow us to study exceptionally problematic cases in which the objective functions are irregular and multimodal, and where information about the extrema location is unobtainable in other ways. Theybelongtotheclassofiterativestochasticoptimizationstrategiesthat, during each step, produce and evaluate the set of admissible points from the search domain, called the random sample or population. As opposed to the Monte Carlo strategies, in which the population is sampled according to the uniform probability distribution over the search domain, genetic algorithms modify the probability distribution at each step. Mechanisms which adopt sampling probability distribution are transposed from biology. They are based mainly on genetic code mutation and crossover, as well as on selection among living individuals. Such mechanisms have been testedbysolvingmultimodalproblemsinnature,whichiscon?rmedinpart- ular by the many species of animals and plants that are well ?tted to di?erent ecological niches. They direct the search process, making it more e?ective than a completely random one (search with a uniform sampling distribution). Moreover,well-tunedgenetic-basedoperationsdonotdecreasetheexploration ability of the whole admissible set, which is vital in the global optimization process. The features described above allow us to regard genetic algorithms as a new class of arti?cial intelligence methods which introduce heuristics, well tested in other ?elds, to the classical scheme of stochastic global search. As opposed to the Monte Carlo strategies, in which the population is sampled according to the uniform probability distribution over the search domain, genetic algorithms modify the probability distribution at each step. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783540731917
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Foundations of Global Genetic Optimization
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Springer, 2007
ISBN 10 : 3540731911
ISBN 13 : 9783540731917
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Foundations of Global Genetic Optimization (Studies in Computational Intelligence, 74)
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Foundations of Global Genetic Optimization
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Springer Berlin Heidelberg Aug 2007, 2007
ISBN 10 : 3540731911
ISBN 13 : 9783540731917
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Genetic algorithms today constitute a family of e ective global optimization methods used to solve di cult real-life problems which arise in science and technology. Despite their computational complexity, they have the ability to explore huge data sets and allow us to study exceptionally problematic cases in which the objective functions are irregular and multimodal, and where information about the extrema location is unobtainable in other ways. Theybelongtotheclassofiterativestochasticoptimizationstrategiesthat, during each step, produce and evaluate the set of admissible points from the search domain, called the random sample or population. As opposed to the Monte Carlo strategies, in which the population is sampled according to the uniform probability distribution over the search domain, genetic algorithms modify the probability distribution at each step. Mechanisms which adopt sampling probability distribution are transposed from biology. They are based mainly on genetic code mutation and crossover, as well as on selection among living individuals. Such mechanisms have been testedbysolvingmultimodalproblemsinnature,whichiscon rmedinpart- ular by the many species of animals and plants that are well tted to di erent ecological niches. They direct the search process, making it more e ective than a completely random one (search with a uniform sampling distribution). Moreover,well-tunedgenetic-basedoperationsdonotdecreasetheexploration ability of the whole admissible set, which is vital in the global optimization process. The features described above allow us to regard genetic algorithms as a new class of arti cial intelligence methods which introduce heuristics, well tested in other elds, to the classical scheme of stochastic global search. 236 pp. Englisch. N° de réf. du vendeur 9783540731917
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Foundations of Global Genetic Optimization
Edité par
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2007
ISBN 10 : 3540731911
ISBN 13 : 9783540731917
Neuf
Couverture rigide
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Etat : New. Focuses on the application of genetic algorithms in continuous global optimization. This book explains the nature of continuous genetic search by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations. Series: Studies in Computational Intelligence. Num Pages: 222 pages, biography. BIC Classification: PSAK. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 235 x 155. . . 2007. Hardback. . . . . N° de réf. du vendeur V9783540731917
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