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Hybrid metaheuristics[electronic resource] :powerful tools for optimization /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.3
書名/作者:	Hybrid metaheuristics : powerful tools for optimization // by Christian Blum, Gunther R. Raidl.
作者:	Blum, Christian.
其他作者:	Raidl, Gunther R.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xvi, 157 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Heuristic programming.
標題:	Mathematical optimization.
標題:	Computer Science.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Theory of Computation.
標題:	Computational Intelligence.
標題:	Operation Research/Decision Theory.
標題:	Optimization.
ISBN:	9783319308838
ISBN:	9783319308821
內容註:	Introduction -- Incomplete Solution Representations and Decoders -- Hybridization Based on Problem Instance Reduction -- Hybridization Based on Large Neighborhood Search -- Making Use of a Parallel, Non-independent, Construction of Solutions Within Metaheuristics -- Hybridization Based on Complete Solution Archives -- Further Hybrids and Conclusions.
摘要、提要註:	This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives. The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.
電子資源:	http://dx.doi.org/10.1007/978-3-319-30883-8

Hybrid metaheuristics[electronic resource] :powerful tools for optimization /
Blum, Christian.

Hybrid metaheuristicspowerful tools for optimization /[electronic resource] :by Christian Blum, Gunther R. Raidl. - Cham :Springer International Publishing :2016. - xvi, 157 p. :ill., digital ;24 cm. - Artificial intelligence: foundations, theory, and algorithms,2365-3051. - Artificial intelligence: foundations, theory, and algorithms..

Introduction -- Incomplete Solution Representations and Decoders -- Hybridization Based on Problem Instance Reduction -- Hybridization Based on Large Neighborhood Search -- Making Use of a Parallel, Non-independent, Construction of Solutions Within Metaheuristics -- Hybridization Based on Complete Solution Archives -- Further Hybrids and Conclusions.

This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives. The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.

ISBN: 9783319308838

Standard No.: 10.1007/978-3-319-30883-8doiSubjects--Topical Terms:

184323
Heuristic programming.

LC Class. No.: T57.84

Dewey Class. No.: 006.3

Hybrid metaheuristics[electronic resource] :powerful tools for optimization /
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