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9783030795559: Metaheuristics for Finding Multiple Solutions (Natural Computing Series)

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ISBN 10: 3030795551 ISBN 13: 9783030795559

Editorial: Springer, 2022

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EditorialSpringer
A�o de publicaci�n2022
ISBN 10 3030795551
ISBN 13 9783030795559
Encuadernaci�nTapa blanda
IdiomaIngl�s
N�mero de edici�n1
N�mero de p�ginas328
EditorPreuss Mike, Epitropakis Michael G., Li Xiaodong, Fieldsend Jonathan E.

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9783030795528: Metaheuristics for Finding Multiple Solutions (Natural Computing Series)

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Metaheuristics for Finding Multiple Solutions (Natural Computing Series)

Publicado por Springer, 2022

ISBN 10: 3030795551 ISBN 13: 9783030795559

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Metaheuristics for Finding Multiple Solutions (Natural Computing Series)

Publicado por Springer, 2022

ISBN 10: 3030795551 ISBN 13: 9783030795559

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Metaheuristics for Finding Multiple Solutions

Publicado por Springer, Berlin|Springer International Publishing|Springer, 2022

ISBN 10: 3030795551 ISBN 13: 9783030795559

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Kartoniert / Broschiert. Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents the latest trends and developments in multimodal optimization and niching techniques. Most existing optimization methods are designed for locating a single global solution. However, in real-world settings, many problems are multimodal. N� de ref. del art�culo: 723462923

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Metaheuristics for Finding Multiple Solutions

Mike Preuss

Publicado por Springer International Publishing, Springer Nature Switzerland Okt 2022, 2022

ISBN 10: 3030795551 ISBN 13: 9783030795559

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Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents the latest trends and developments in multimodal optimization and niching techniques. Most existing optimization methods are designed for locating a single global solution. However, in real-world settings, many problems are 'multimodal' by nature, i.e., multiple satisfactory solutions exist.It may be desirable to locate several such solutions before deciding which one to use.Multimodal optimization has been the subject of intense study in the field of population-based meta-heuristic algorithms, e.g., evolutionary algorithms (EAs), for the past few decades.These multimodal optimization techniques are commonly referred to as 'niching' methods, because of the nature-inspired 'niching' effect that is induced to the solution population targeting at multiple optima. Many niching methods have been developed in the EA community. Some classic examples include crowding, fitness sharing, clearing, derating, restricted tournament selection, speciation, etc.Nevertheless, applying these niching methods to real-world multimodal problems often encounters significant challenges.To facilitate the advance of niching methods in facing these challenges, this edited book highlights the latest developments in niching methods. The included chapters touch on algorithmic improvements and developments, representation, and visualization issues, as well as new research directions, such as preference incorporation in decision making and new application areas. This edited book is a first of this kind specifically on the topic of niching techniques.This book will serve as a valuable reference book both for researchers and practitioners. Although chapters are written in a mutually independent way, Chapter 1 will help novice readers get an overview of the field. It describes the development of the field and its current state and provides a comparative analysis of the IEEE CEC and ACM GECCO niching competitions of recent years, followed by a collection of open research questions and possible research directions that may be tackled in the future. 328 pp. Englisch. N� de ref. del art�culo: 9783030795559

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Metaheuristics for Finding Multiple Solutions

Mike Preuss

Publicado por Springer International Publishing, Springer Nature Switzerland, 2022

ISBN 10: 3030795551 ISBN 13: 9783030795559

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Librería: AHA-BUCH GmbH, Einbeck, Alemania

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Taschenbuch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the latest trends and developments in multimodal optimization and niching techniques. Most existing optimization methods are designed for locating a single global solution. However, in real-world settings, many problems are 'multimodal' by nature, i.e., multiple satisfactory solutions exist.It may be desirable to locate several such solutions before deciding which one to use.Multimodal optimization has been the subject of intense study in the field of population-based meta-heuristic algorithms, e.g., evolutionary algorithms (EAs), for the past few decades.These multimodal optimization techniques are commonly referred to as 'niching' methods, because of the nature-inspired 'niching' effect that is induced to the solution population targeting at multiple optima. Many niching methods have been developed in the EA community. Some classic examples include crowding, fitness sharing, clearing, derating, restricted tournament selection, speciation, etc.Nevertheless, applying these niching methods to real-world multimodal problems often encounters significant challenges.To facilitate the advance of niching methods in facing these challenges, this edited book highlights the latest developments in niching methods. The included chapters touch on algorithmic improvements and developments, representation, and visualization issues, as well as new research directions, such as preference incorporation in decision making and new application areas. This edited book is a first of this kind specifically on the topic of niching techniques.This book will serve as a valuable reference book both for researchers and practitioners. Although chapters are written in a mutually independent way, Chapter 1 will help novice readers get an overview of the field. It describes the development of the field and its current state and provides a comparative analysis of the IEEE CEC and ACM GECCO niching competitions of recent years, followed by a collection of open research questions and possible research directions that may be tackled in the future. N� de ref. del art�culo: 9783030795559

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