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Serie: Studies in Computational Intelligence, Buch 146 von 538. Buch 146 von 538 - Studies in Computational Intelligence
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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.
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Verlag: Springer-Verlag New York Inc 2016
Serie: Studies in Computational Intelligence, Buch 146 von 538. Buch 146 von 538 - Studies in Computational Intelligence
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Taschenbuch. Zustand: Neu. Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization | Godfrey C. Onwubolu (u. a.) | Taschenbuch | xvii | Englisch | 2017 | Springer | EAN 9783662518922 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hart…mann[at]springer[dot]com | Anbieter: preigu.
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Bundle. Zustand: New. 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 .
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing 2018
Serie: Studies in Computational Intelligence, Buch 146 von 538. Buch 146 von 538 - Studies in Computational Intelligence
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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 b…ook 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.
Sprache: Englisch
Verlag: Springer International Publishing 2016
Serie: Studies in Computational Intelligence, Buch 146 von 538. Buch 146 von 538 - Studies in Computational Intelligence
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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.
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Taschenbuch. 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 sh…ould 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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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 vi…sit 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.
Sprache: Englisch
Verlag: Palgrave Macmillan 2016
Serie: Studies in Computational Intelligence, Buch 146 von 538. Buch 146 von 538 - Studies in Computational Intelligence
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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 308 | Sprache: Englisch | Produktart: Bücher | 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 t…he 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.
