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A Practical Framework for Adaptive Metaheuristics - Varrentrapp, Klaus
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Varrentrapp, Klaus:

A Practical Framework for Adaptive Metaheuristics - Taschenbuch

2008, ISBN: 3639034562, Lieferbar binnen 4-6 Wochen Versandkosten:Versandkostenfrei innerhalb der BRD

ID: 9783639034561

Internationaler Buchtitel. In englischer Sprache. Verlag: VDM Verlag, Paperback, 380 Seiten, L=220mm, B=150mm, H=23mm, Gew.=582gr, [GR: 16390 - HC/Informatik/EDV/Sonstiges], Kartoniert/Broschiert, Klappentext: Local search methods (LSM) such as metaheuristics are useful tools to solve hard combinatorial optimization problems (COP). LSM can be further improved by identifying and exploiting regularities. This can be achieved by means of machine learning (ML) techniques to extend existing LSM. The author overviews how to integrate ML techniques into LSM to solve COP where only evaluative instead of constructive feedback can be obtained. A theoretical framework called Guided Adaptive Iterated Local Search (GAILS) is developed and investigated which demonstrates how to apply Reinforcement Learning (RL) to adaptively solve COP. The framework extends the well known and powerful metaheuristic Iterated Local Search and establishes the notion of a Local Search Agent that can be considered as an agent in a RL problem. An accompanying practical implementation framework for arbitrary GAILS algorithms is presented and theoretically as well as experimentally investigated. This book is written for readers interested in adaptive local search methods and the application of ML techniques in combinatorial optimization. Local search methods (LSM) such as metaheuristics are useful tools to solve hard combinatorial optimization problems (COP). LSM can be further improved by identifying and exploiting regularities. This can be achieved by means of machine learning (ML) techniques to extend existing LSM. The author overviews how to integrate ML techniques into LSM to solve COP where only evaluative instead of constructive feedback can be obtained. A theoretical framework called Guided Adaptive Iterated Local Search (GAILS) is developed and investigated which demonstrates how to apply Reinforcement Learning (RL) to adaptively solve COP. The framework extends the well known and powerful metaheuristic Iterated Local Search and establishes the notion of a Local Search Agent that can be considered as an agent in a RL problem. An accompanying practical implementation framework for arbitrary GAILS algorithms is presented and theoretically as well as experimentally investigated. This book is written for readers interested in adaptive local search methods and the application of ML techniques in combinatorial optimization.

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A Practical Framework for Adaptive Metaheuristics - Klaus Varrentrapp
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Klaus Varrentrapp:

A Practical Framework for Adaptive Metaheuristics - Taschenbuch

ISBN: 3639034562

Paperback, [EAN: 9783639034561], VDM Verlag, VDM Verlag, Book, [PU: VDM Verlag], VDM Verlag, Local search methods (LSM) such as metaheuristics are useful tools to solve hard combinatorial optimization problems (COP). LSM can be further improved by identifying and exploiting regularities. This can be achieved by means of machine learning (ML) techniques to extend existing LSM.The author overviews how to integrate ML techniques into LSM to solve COP where only evaluative instead of constructive feedback can be obtained. A theoretical framework called Guided Adaptive Iterated Local Search (GAILS) is developed and investigated which demonstrates how to apply Reinforcement Learning (RL) to adaptively solve COP. The framework extends the well known and powerful metaheuristic Iterated Local Search and establishes the notion of a Local Search Agent that can be considered as an agent in a RL problem. An accompanying practical implementation framework for arbitrary GAILS algorithms is presented and theoretically as well as experimentally investigated.This book is written for readers interested in adaptive local search methods and the application of ML techniques in combinatorial optimization., 5, Computers & Technology, 69765, Home Computing, 15377001, Apple, 549726, Business & Culture, 3448, Certification, 3508, Computer Science, 549646, Databases, 15375291, Digital Music, 377542011, Digital Photography & Video, 87, Games & Strategy Guides, 4134, Graphic Design, 549652, Hardware, 285844, Microsoft, 377557011, Mobile & Wireless Computing, 3652, Networking, 3756, Operating Systems, 3839, Programming, 377567011, Project Management, 377560011, Security & Encryption, 4053, Software, 3510, Web Development, 1000, Subjects, 283155, Books, 468204, Computer Science, 491298, Algorithms, 491300, Artificial Intelligence, 491306, Database Storage & Design, 491308, Graphics & Visualization, 491302, Networking, 491310, Object-Oriented Software Design, 491312, Operating Systems, 491314, Programming Languages, 491316, Software Design & Engineering, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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ISBN: 9783639034561

Klaus Varrentrapp,Paperback, English-language edition,Pub by VDM Verlag Dr. Mueller e.K. Books Books ~~ Computers~~ General A-Practical-Framework-For-Adaptive-Metaheuristics~~Klaus-Varrentrapp VDM Verlag Dr. Mueller e.K. Local search methods (LSM) such as metaheuristics are useful tools to solve hard combinatorial optimization problems (COP). LSM can be further improved by identifying and exploiting regularities. This can be achieved by means of machine learning (ML) techniques to extend existing LSM. The author overviews how to integrate ML techniques into LSM to solve COP where only evaluative instead of constructive feedback can be obtained. A theoretical framework called Guided Adaptive Iterated Local Search (GAILS) is developed and investigated which demonstrates how to apply Reinforcement Learning (RL) to adaptively solve COP. The framework extends the well known and powerful metaheuristic Iterated Local Search and establishes the notion of a Local Search Agent that can be considered as an agent in a RL problem. An accompanying practical implementation framework for arbitrary GAILS algorithms is presented and theoretically as well as experimentally investigated. This book is written for readers interested in adaptive local search methods and the application of ML techniques in combinatorial optimization.

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A Practical Framework for Adaptive Metaheuristics
Autor:

Varrentrapp, Klaus

Titel:

A Practical Framework for Adaptive Metaheuristics

ISBN-Nummer:

9783639034561

Local search methods (LSM) such as metaheuristics are useful tools to solve hard combinatorial optimization problems (COP). LSM can be further improved by identifying and exploiting regularities. This can be achieved by means of machine learning (ML) techniques to extend existing LSM. The author overviews how to integrate ML techniques into LSM to solve COP where only evaluative instead of constructive feedback can be obtained. A theoretical framework called Guided Adaptive Iterated Local Search (GAILS) is developed and investigated which demonstrates how to apply Reinforcement Learning (RL) to adaptively solve COP. The framework extends the well known and powerful metaheuristic Iterated Local Search and establishes the notion of a Local Search Agent that can be considered as an agent in a RL problem. An accompanying practical implementation framework for arbitrary GAILS algorithms is presented and theoretically as well as experimentally investigated. This book is written for readers interested in adaptive local search methods and the application of ML techniques in combinatorial optimization.

Detailangaben zum Buch - A Practical Framework for Adaptive Metaheuristics


EAN (ISBN-13): 9783639034561
ISBN (ISBN-10): 3639034562
Taschenbuch
Erscheinungsjahr: 2008
Herausgeber: VDM Verlag
380 Seiten
Gewicht: 0,582 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 10.09.2009 13:20:06
Buch zuletzt gefunden am 10.10.2014 07:49:32
ISBN/EAN: 9783639034561

ISBN - alternative Schreibweisen:
3-639-03456-2, 978-3-639-03456-1

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