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Evolutionary Learning: Advances in Theories and Algorithms

Zhou

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ISBN 10 : 9811359555 ISBN 13 : 9789811359552

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Evolutionary Learning: Advances in Theories and Algorithms

Zhou

Edit� par Springer, 2019

ISBN 10 : 9811359555 ISBN 13 : 9789811359552

Neuf Couverture rigide

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Evolutionary Learning: Advances in Theories and Algorithms

Zhi-Hua Zhou

Edit� par Springer Nature Singapore Jun 2019, 2019

ISBN 10 : 9811359555 ISBN 13 : 9789811359552

Neuf Couverture rigide

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance. 376 pp. Englisch. N� de r�f. du vendeur 9789811359552

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Evolutionary Learning: Advances in Theories and Algorithms

Zhi-Hua Zhou|Yang Yu|Chao Qian

Edit� par Springer Nature Singapore, 2019

ISBN 10 : 9811359555 ISBN 13 : 9789811359552

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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents theoretical results for evolutionary learningProvides general theoretical tools for analysing evolutionary algorithmsProposes evolutionary learning algorithms with provable theoretical guarantees&nbsp&nbspPresents theo. N� de r�f. du vendeur 257581037

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Evolutionary Learning: Advances in Theories and Algorithms

Zhi-Hua Zhou

Edit� par Springer Nature Singapore, Springer Nature Singapore, 2019

ISBN 10 : 9811359555 ISBN 13 : 9789811359552

Neuf Couverture rigide

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance. N� de r�f. du vendeur 9789811359552

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