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Añadir al carritoHardcover. Condición: Very Good. 880 Pages Usual Ex-Lib Marks O/W Sound.
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Añadir al carritoHardcover. Condición: 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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Añadir al carritoCondición: 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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Publicado por Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2017
ISBN 10: 3662518929 ISBN 13: 9783662518922
Idioma: Inglés
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Añadir al carritoPaperback. Condición: new. Paperback. 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 problems 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 problems goal is to pack objects of differing weight and value so that the knapsacks 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. The traveling salesman problems feasible solutions - permutations of city labels - c- prise a ?nite, discrete set. The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2009
ISBN 10: 3540921508 ISBN 13: 9783540921509
Idioma: Inglés
Librería: Grand Eagle Retail, Mason, OH, Estados Unidos de America
EUR 118,36
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Añadir al carritoHardcover. Condición: new. Hardcover. 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 problems 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 problems goal is to pack objects of differing weight and value so that the knapsacks 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. The traveling salesman problems feasible solutions - permutations of city labels - c- prise a ?nite, discrete set. The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Publicado por Springer Berlin Heidelberg, Springer Berlin Heidelberg Jan 2009, 2009
ISBN 10: 3540921508 ISBN 13: 9783540921509
Idioma: Inglés
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 106,99
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Añadir al carritoBuch. Condición: 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.
Publicado por Springer Berlin Heidelberg, Springer Berlin Heidelberg Apr 2017, 2017
ISBN 10: 3662518929 ISBN 13: 9783662518922
Idioma: Inglés
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 106,99
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Añadir al carritoTaschenbuch. Condición: 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.
Publicado por Springer Berlin Heidelberg, 2017
ISBN 10: 3662518929 ISBN 13: 9783662518922
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 106,99
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Añadir al carritoTaschenbuch. Condición: 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.
Publicado por Springer Berlin Heidelberg, 2009
ISBN 10: 3540921508 ISBN 13: 9783540921509
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 106,99
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Añadir al carritoBuch. Condición: 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.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 150,78
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Añadir al carritoPaperback. Condición: Brand New. reprint edition. 232 pages. 9.25x6.10x0.53 inches. In Stock.
Publicado por Springer Berlin Heidelberg, 2009
ISBN 10: 3540921508 ISBN 13: 9783540921509
Idioma: Inglés
Librería: Buchpark, Trebbin, Alemania
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Añadir al carritoCondición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. hardback/cd-rom edition. 211 pages. 9.50x6.50x0.50 inches. In Stock.
Publicado por Springer-Verlag New York Inc., New York, NY, 2010
ISBN 10: 1441948120 ISBN 13: 9781441948120
Idioma: Inglés
Librería: Grand Eagle Retail, Mason, OH, Estados Unidos de America
Original o primera edición
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Añadir al carritoPaperback. Condición: new. Paperback. Significant research activities have taken place in the areas of local and global optimization in the last two decades. Many new theoretical, computational, algorithmic, and software contributions have resulted. It has been realized that despite these numerous contributions, there does not exist a systematic forum for thorough experimental computational testing and evaluation of the proposed optimization algorithms and their implementations. Well-designed nonconvex optimization test problems are of major impor tance for academic and industrial researchers interested in algorithmic and software development. It is remarkable that eventhough nonconvex models dominate all the important application areas in engineering and applied sci ences, there is only a limited dass of reported representative test problems. This book reflects our long term efforts in designing a benchmark database and it is motivated primarily from the need for nonconvex optimization test problems. The present collection of benchmarks indudes test problems from literature studies and a large dass of applications that arise in several branches of engineering and applied science. Well-designed nonconvex optimization test problems are of major impor tance for academic and industrial researchers interested in algorithmic and software development. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.