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Aggiungi al carrelloHardcover. Condizione: Très bon. Ancien livre de bibliothèque. Edition 1999. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1999. Ammareal gives back up to 15% of this item's net price to charity organizations.
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Handbook of Global Optimization (Nonconvex Optimization and Its Applications)
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Differential Evolution A Handbook For Global Permutation Based Combinatorial Optimization (Hb 2009)
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Differential Evolution: A Handbook For Global Permutation-Based Combinatorial Optimization (Hb)
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Handbook of Global Optimization (Nonconvex Optimization and Its Applications)
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Aggiungi al carrelloCondizione: Gut. Auflage: 1995. 898 Seiten Exemplar aus einer wissenchaftlichen Bibliothek Sprache: Englisch Gewicht in Gramm: 1494 25,4 x 17,8 x 5,7 cm, Gebundene Ausgabe.
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Edizione InternazionaleDIFFERENTIAL EVOLUTION A HANDBOOK FOR GLOBAL PERMUTATION BASED COMBINATORIAL OPTIMIZATION (HB 2009)
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Differential Evolution: A Handbook For Global Permutation-based Combinatorial Optimization
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Differential Evolution: A Handbook For Global Permutation-based Combinatorial Optimization
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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 (Studies in Computational Intelligence, 175)
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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 (Studies in Computational Intelligence, 175)
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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 (Studies in Computational Intelligence, 175)
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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
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Differential Evolution: A Handbook for Global Permutation-based Combinatorial Optimization
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Aggiungi al carrelloPaperback. Condizione: Brand New. reprint edition. 232 pages. 9.25x6.10x0.53 inches. In Stock.
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Differential Evolution: A Handbook for Global Permutation-based Combinatorial Optimization
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Aggiungi al carrelloHardcover. Condizione: Brand New. hardback/cd-rom edition. 211 pages. 9.50x6.50x0.50 inches. In Stock.
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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Lingua: Inglese
Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Apr 2017, 2017
ISBN 10: 3662518929 ISBN 13: 9783662518922
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Lingua: Inglese
Editore: Springer Berlin Heidelberg, 2017
ISBN 10: 3662518929 ISBN 13: 9783662518922
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization : A Handbook for Global Permutation-Based Combinatorial Optimization
Lingua: Inglese
Editore: Springer, Berlin, Springer, 2009
ISBN 10: 3540921508 ISBN 13: 9783540921509
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Aggiungi al carrelloSonstige Video. Condizione: 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.
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.
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Handbook of Test Problems in Local and Global Optimization (Nonconvex Optimization and Its Applications)
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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Handbook of Global Optimization
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Handbook of Global Optimization: Volume 2 (Nonconvex Optimization and Its Applications)
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Handbook of Global Optimization: Volume 2 (Nonconvex Optimization and Its Applications, 62)
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Handbook of Test Problems in Local and Global Optimization (Nonconvex Optimization and Its Applications, 33)
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