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Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: 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.…

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Hardcover. Zustand: Brand New. hardback/cd-rom edition. 211 pages. 9.50x6.50x0.50 inches. In Stock.

Hardcover. Zustand: Brand New. 9.50x6.50x1.00 inches. In Stock.

Taschenbuch. Zustand: Neu. Self-Organizing Migrating Algorithm | Methodology and Implementation | Donald Davendra (u. a.) | Taschenbuch | xviii | Englisch | 2018 | Springer | EAN 9783319802862 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book brings together the current stateof-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novelpopulation-based evolutionary algorithm, modeled on the predator-preyrelationship, by its leading practitioners.As the first ever book on SOMA, this book isgeared towards graduate students, academics and researchers, who are looking fora good optimization algorithm for their applications. This book presents themethodology of SOMA, covering both the real and discrete domains, and itsvarious implementations in different research areas. The easy-to-follow andimplement methodology used in the book will make it easier for a reader toimplement, modify and utilize SOMA.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book brings together the current stateof-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novelpopulation-based evolutionary algorithm, modeled on the predator-preyrelationship, by its leading practitioners.As the first ever book on SOMA, this book isgeared towards graduate students, academics and researchers, who are looking fora good optimization algorithm for their applications. This book presents themethodology of SOMA, covering both the real and discrete domains, and itsvarious implementations in different research areas. The easy-to-follow andimplement methodology used in the book will make it easier for a reader toimplement, modify and utilize SOMA.…

Buch. Zustand: 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.…