Isbn: 9783030294168 - theory of evolutionary computation: recent developments in discrete optimization (14 risultati)

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  • Lingua: Inglese

    Editore: Springer Nature Switzerland AG, Cham, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Paperback. Condizione: new. Paperback. This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influencesperformance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming.Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing. This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

  • Lingua: Inglese

    Editore: Springer, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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  • Lingua: Inglese

    Editore: Springer, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Taschenbuch. Condizione: Neu. Theory of Evolutionary Computation | Recent Developments in Discrete Optimization | Benjamin Doerr (u. a.) | Taschenbuch | Natural Computing Series | xii | Englisch | 2020 | Springer | EAN 9783030294168 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

  • Lingua: Inglese

    Editore: Springer, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Condizione: New. In English.

  • Lingua: Inglese

    Editore: Springer, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Condizione: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.

  • Lingua: Inglese

    Editore: Springer-Nature New York Inc, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Paperback. Condizione: Brand New. 506 pages. 9.25x6.10x1.26 inches. In Stock.

  • Lingua: Inglese

    Editore: Springer Nature Switzerland AG, Cham, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Paperback. Condizione: new. Paperback. This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influencesperformance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming.Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing. This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

  • Lingua: Inglese

    Editore: Springer, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics.It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influencesperformance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming.Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing.

  • Lingua: Inglese

    Editore: Springer, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Condizione: new. Questo è un articolo print on demand.

  • Lingua: Inglese

    Editore: Springer International Publishing, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Many advances have been made in this field in the last ten yearsConcise summary of the state of the art for graduate students and researchers Book covers the development of more powerful methods, the solution of longstanding open problems, and th.

  • Lingua: Inglese

    Editore: Springer International Publishing Dez 2020, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics.It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influencesperformance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming.Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing. 532 pp. Englisch.

  • Lingua: Inglese

    Editore: Springer, Springer Dez 2020, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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    Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics.It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influencesperformance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming.Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 532 pp. Englisch.

  • Lingua: Inglese

    Editore: Springer, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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  • Lingua: Inglese

    Editore: Springer, 2020

    3030294161 / 9783030294168

    Serie: Libro 31 di 32 - Natural Computing

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