Davendra Donald (54 results)

xviii, 289 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

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Taschenbuch. Condition: 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]hartmann[at]springer[dot]com | Anbieter: preigu.

Bundle. Condition: 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 .

Taschenbuch. Condition: 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.

Taschenbuch. Condition: 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. Condition: 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.

Taschenbuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch.

Buch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch.

Buch. Condition: Neu. Neuware -The Traveling Salesman Problem (TSP) is widely considered one of the most intensively studied problems in computational mathematics and operations research. Since its inception, it has become the poster child for computational complexity research. A number of problems have been transformed to a TSP problem and its application base now extends into scheduling, manufacturing, routing, and logistics. With the advent of high-performance computing and advanced meta-heuristics such as GPU programming and swarm-based algorithms, the TSP problem is positioned firmly as the go-to problem for the development of the next generation of high-performance intelligent heuristics. This book looks to leverage some of these new paradigms for both students and researchers in this field.Books on Demand GmbH, Überseering 33, 22297 Hamburg 100 pp. Englisch.

Sonstige Video. Condition: 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.

Taschenbuch. Condition: 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.

Condition: 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.