Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization - Couverture souple

9783662518922: Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization

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ISBN 10 : 3662518929 ISBN 13 : 9783662518922

Editeur : Springer, 2017

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What is combinatorial optimization? Traditionally, a problem is considered to be c- binatorial if its set of feasible solutions is both ?nite and discrete, i. e. , enumerable. For example, the traveling salesman problem asks in what order a salesman should visit the cities in his territory if he wants to minimize his total mileage (see Sect. 2. 2. 2). The traveling salesman problem’s feasible solutions - permutations of city labels - c- prise a ?nite, discrete set. By contrast, Differential Evolution was originally designed to optimize functions de?ned on real spaces. Unlike combinatorial problems, the set of feasible solutions for real parameter optimization is continuous. Although Differential Evolution operates internally with ?oating-point precision, it has been applied with success to many numerical optimization problems that have t- ditionally been classi?ed as combinatorial because their feasible sets are discrete. For example, the knapsack problem’s goal is to pack objects of differing weight and value so that the knapsack’s total weight is less than a given maximum and the value of the items inside is maximized (see Sect. 2. 2. 1). The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. To handle such problems while retaining full precision, Differential Evolution copies ?oating-point - lutions to a temporary vector that, prior to being evaluated, is truncated to the nearest feasible solution, e. g. , by rounding the temporary parameters to the nearest nonnegative integer.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

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This book, the first devoted entirely to the subject, presents in detail the various permutative-based combinatorial differential evolution formulations by their initiators in an easy-to-follow manner through numerous illustrations and computer code.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer
Date d'�dition: 2017
Langue: anglais
ISBN 10: 3662518929
ISBN 13: 9783662518922
Reliure: Broch�
Nombre de pages: 232
�diteur: Onwubolu Godfrey C., Davendra Donald
Coordonn�es du fabricant: non disponible

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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization

Onwubolu, Godfrey C.|Davendra, Donald

Edit� par Springer Berlin Heidelberg, 2017

ISBN 10 : 3662518929 ISBN 13 : 9783662518922

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a complete introduction to differential evolutionIncludes the continuous space DE formulation and the permutative-based combinatorial DE formulationWhat is combinatorial optimization? Traditionally, a problem is considered . N� de r�f. du vendeur 385771887

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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization

Donald Davendra

Edit� par Springer Berlin Heidelberg, 2017

ISBN 10 : 3662518929 ISBN 13 : 9783662518922

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - What is combinatorial optimization Traditionally, a problem is considered to be c- binatorial if its set of feasible solutions is both nite and discrete, i. e. , enumerable. For example, the traveling salesman problem asks in what order a salesman should visit the cities in his territory if he wants to minimize his total mileage (see Sect. 2. 2. 2). The traveling salesman problem's feasible solutions - permutations of city labels - c- prise a nite, discrete set. By contrast, Differential Evolution was originally designed to optimize functions de ned on real spaces. Unlike combinatorial problems, the set of feasible solutions for real parameter optimization is continuous. Although Differential Evolution operates internally with oating-point precision, it has been applied with success to many numerical optimization problems that have t- ditionally been classi ed as combinatorial because their feasible sets are discrete. For example, the knapsack problem's goal is to pack objects of differing weight and value so that the knapsack's total weight is less than a given maximum and the value of the items inside is maximized (see Sect. 2. 2. 1). The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. To handle such problems while retaining full precision, Differential Evolution copies oating-point - lutions to a temporary vector that, prior to being evaluated, is truncated to the nearest feasible solution, e. g. , by rounding the temporary parameters to the nearest nonnegative integer. N� de r�f. du vendeur 9783662518922

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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization

Donald Davendra

Edit� par Springer Berlin Heidelberg Apr 2017, 2017

ISBN 10 : 3662518929 ISBN 13 : 9783662518922

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -What is combinatorial optimization Traditionally, a problem is considered to be c- binatorial if its set of feasible solutions is both nite and discrete, i. e. , enumerable. For example, the traveling salesman problem asks in what order a salesman should visit the cities in his territory if he wants to minimize his total mileage (see Sect. 2. 2. 2). The traveling salesman problem's feasible solutions - permutations of city labels - c- prise a nite, discrete set. By contrast, Differential Evolution was originally designed to optimize functions de ned on real spaces. Unlike combinatorial problems, the set of feasible solutions for real parameter optimization is continuous. Although Differential Evolution operates internally with oating-point precision, it has been applied with success to many numerical optimization problems that have t- ditionally been classi ed as combinatorial because their feasible sets are discrete. For example, the knapsack problem's goal is to pack objects of differing weight and value so that the knapsack's total weight is less than a given maximum and the value of the items inside is maximized (see Sect. 2. 2. 1). The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. To handle such problems while retaining full precision, Differential Evolution copies oating-point - lutions to a temporary vector that, prior to being evaluated, is truncated to the nearest feasible solution, e. g. , by rounding the temporary parameters to the nearest nonnegative integer. 232 pp. Englisch. N� de r�f. du vendeur 9783662518922

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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization (Studies in Computational Intelligence, 175)

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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization

Donald Davendra

Edit� par Springer Berlin Heidelberg, Springer Berlin Heidelberg Apr 2017, 2017

ISBN 10 : 3662518929 ISBN 13 : 9783662518922

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. Neuware -What is combinatorial optimization Traditionally, a problem is considered to be c- binatorial if its set of feasible solutions is both nite and discrete, i. e. , enumerable. For example, the traveling salesman problem asks in what order a salesman should visit the cities in his territory if he wants to minimize his total mileage (see Sect. 2. 2. 2). The traveling salesman problem�s feasible solutions - permutations of city labels - c- prise a nite, discrete set. By contrast, Differential Evolution was originally designed to optimize functions de ned on real spaces. Unlike combinatorial problems, the set of feasible solutions for real parameter optimization is continuous. Although Differential Evolution operates internally with oating-point precision, it has been applied with success to many numerical optimization problems that have t- ditionally been classi ed as combinatorial because their feasible sets are discrete. For example, the knapsack problem�s goal is to pack objects of differing weight and value so that the knapsack�s total weight is less than a given maximum and the value of the items inside is maximized (see Sect. 2. 2. 1). The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. To handle such problems while retaining full precision, Differential Evolution copies oating-point - lutions to a temporary vector that, prior to being evaluated, is truncated to the nearest feasible solution, e. g. , by rounding the temporary parameters to the nearest nonnegative integer.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 232 pp. Englisch. N� de r�f. du vendeur 9783662518922

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