Articles liés à A Brief Introduction to Continuous Evolutionary Optimization
Synopsis
Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.
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- ÉditeurSpringer
- Date d'édition2013
- ISBN 10 3319034219
- ISBN 13 9783319034218
- ReliureBroché
- Langueanglais
- Nombre de pages108
- Coordonnées du fabricantnon disponible
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A Brief Introduction to Continuous Evolutionary Optimization
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Springer International Publishing, 2013
ISBN 10 : 3319034219
ISBN 13 : 9783319034218
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Etat : Sehr gut. Zustand: Sehr gut | Seiten: 108 | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 24395598/12
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A Brief Introduction to Continuous Evolutionary Optimization
Edité par
Springer International Publishing, 2013
ISBN 10 : 3319034219
ISBN 13 : 9783319034218
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spacesIntroduction to evolution strategies and parameter controlPresents heuristic extensions that allow optimization in . N° de réf. du vendeur 4496678
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A Brief Introduction to Continuous Evolutionary Optimization
ISBN 10 : 3319034219
ISBN 13 : 9783319034218
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods. N° de réf. du vendeur 9783319034218
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A Brief Introduction to Continuous Evolutionary Optimization
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Springer International Publishing Dez 2013, 2013
ISBN 10 : 3319034219
ISBN 13 : 9783319034218
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods. 108 pp. Englisch. N° de réf. du vendeur 9783319034218
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A Brief Introduction to Continuous Evolutionary Optimization
ISBN 10 : 3319034219
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Taschenbuch. Etat : Neu. Neuware -Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 108 pp. Englisch. N° de réf. du vendeur 9783319034218
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