9789811648588 - genetic programming for production scheduling: an evolutionary learning approach (machine learning: foundations, methodologies, and applications) de zhang, fangfang; nguyen, su; mei, yi (15 resultados)
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Editorial: Springer, 2021
Serie: Libro 1 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
Idioma: Inglés
Editorial: Springer Nature Singapore, Springer Nature Singapore, 2021
Serie: Libro 1 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Buch. Condición: 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 met…hods, 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.
Idioma: Inglés
Editorial: Springer-Nature New York Inc, 2021
Serie: Libro 1 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Hardcover. Condición: Brand New. 369 pages. 9.25x6.10x9.21 inches. In Stock.
Idioma: Inglés
Editorial: Springer, 2021
Serie: Libro 1 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condición: new. Questo è un articolo print on demand.
Idioma: Inglés
Editorial: Springer Nature Singapore Nov 2021, 2021
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condición: 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, exist…ing 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.
Idioma: Inglés
Editorial: Springer, Berlin|Springer Nature Singapore|Springer, 2021
Serie: Libro 1 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Condición: 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. In Part I, it provides an introduction to production scheduling, existing solution m…ethods, and the GP approach to productio.
Idioma: Inglés
Editorial: Springer, 2021
Serie: Libro 1 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Editorial: Springer, Springer Nov 2021, 2021
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 372 pp. Englisch.
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Serie: Libro 1 de 4 - Machine Learning: Foundations, Methodologies, and Applications
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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