Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Genetic and Evolutionary Computation)

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Saxena, Dhish Kumar; Mittal, Sukrit; Deb, Kalyanmoy; Goodman, Erik D.

Publicado por Springer, 2024

ISBN 10: 9819920957 ISBN 13: 9789819920952

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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization

Saxena, Dhish Kumar|Mittal, Sukrit|Deb, Kalyanmoy|Goodman, Erik D.

Publicado por Springer, Berlin|Springer Nature Singapore|Springer, 2023

ISBN 10: 9819920957 ISBN 13: 9789819920952

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Gebunden. Condici�n: New. N� de ref. del art�culo: 838236801

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Machine Learning Assisted Evolutionary Multi- And Many- Objective Optimization

Dhish Kumar Saxena

Publicado por Apress Mrz 2024, 2024

ISBN 10: 9819920957 ISBN 13: 9789819920952

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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

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Buch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EM�O). EM�O algorithms, namely EM�OAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EM�OAs amenable to application of ML for different pursuits.Recognizing the immense potential for ML-based enhancements in the EM�O domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners.To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types.Then, well-structured chapters present some of the key studies on ML-based enhancements in the EM�O domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23). It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EM�OAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EM�OA domain.To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EM�OA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EM�OA and ML domains. 244 pp. Englisch. N� de ref. del art�culo: 9789819920952

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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Genetic and Evolutionary Computation)

Saxena, Dhish Kumar; Mittal, Sukrit; Deb, Kalyanmoy; Goodman, Erik D.

Publicado por Springer, 2024

ISBN 10: 9819920957 ISBN 13: 9789819920952

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Librería: Ria Christie Collections, Uxbridge, Reino Unido

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Condici�n: New. In. N� de ref. del art�culo: ria9789819920952_new

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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization

Dhish Kumar Saxena

Publicado por Springer Nature Singapore, 2024

ISBN 10: 9819920957 ISBN 13: 9789819920952

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Librería: AHA-BUCH GmbH, Einbeck, Alemania

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Buch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EM�O). EM�O algorithms, namely EM�OAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EM�OAs amenable to application of ML for different pursuits.Recognizing the immense potential for ML-based enhancements in the EM�O domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners.To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types.Then, well-structured chapters present some of the key studies on ML-based enhancements in the EM�O domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23). It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EM�OAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EM�OA domain.To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EM�OA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EM�OA and ML domains. N� de ref. del art�culo: 9789819920952

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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Hardcover)

Dhish Kumar Saxena

Publicado por Springer Verlag, Singapore, Singapore, 2024

ISBN 10: 9819920957 ISBN 13: 9789819920952

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Librería: CitiRetail, Stevenage, Reino Unido

Calificaci�n del vendedor: 5 de 5 estrellas

Hardcover. Condici�n: new. Hardcover. This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMaO). EMaO algorithms, namely EMaOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMaOAs amenable to application of ML for different pursuits. Recognizing the immense potential for ML-based enhancements in the EMaO domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners. To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types. Then, well-structured chapters present some of the key studies on ML-based enhancements in the EMaO domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23). It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EMaOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMaOA domain.To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EMaOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMaOA and ML domains. This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMaO). Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMaOA domain.To aid readers, the book includes working codes for the developed algorithms. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de ref. del art�culo: 9789819920952

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