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Genetic Programming for Production Scheduling: An Evolutionary Learning Approach (Machine Learning: Foundations, Methodologies, and Applications) - Softcover
- PublisherSpringer
- Publication date2022
- ISBN 10 9811648611
- ISBN 13 9789811648618
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages372
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Genetic Programming for Production Scheduling : An Evolutionary Learning Approach
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Springer Nature Singapore, 2022
ISBN 10: 9811648611
ISBN 13: 9789811648618
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Genetic Programming for Production Scheduling: An Evolutionary Learning Approach (Machine Learning: Foundations, Methodologies, and Applications)
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Genetic Programming for Production Scheduling
Published by
Springer Nature Singapore Nov 2022, 2022
ISBN 10: 9811648611
ISBN 13: 9789811648618
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future.Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering. 372 pp. Englisch. Seller Inventory # 9789811648618
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Genetic Programming for Production Scheduling
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Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811648611
ISBN 13: 9789811648618
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution me. Seller Inventory # 738838615
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Genetic Programming for Production Scheduling: An Evolutionary Learning Approach (Machine Learning: Foundations, Methodologies, and Applications)
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Genetic Programming for Production Scheduling : An Evolutionary Learning Approach
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Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811648611
ISBN 13: 9789811648618
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Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future.Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering. Seller Inventory # 9789811648618
Quantity: 1 available
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Genetic Programming for Production Scheduling: An Evolutionary Learning Approach (Machine Learning: Foundations, Methodologies, and Applications)
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Paperback. Condition: Brand New. 369 pages. 9.25x6.10x0.91 inches. In Stock. Seller Inventory # x-9811648611
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