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Hybrid metaheuristics = powerful too...

Blum, Christian.

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Hybrid metaheuristics = powerful tools for optimization /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hybrid metaheuristics/ by Christian Blum, Gunther R. Raidl.
Reminder of title:	powerful tools for optimization /
Author:	Blum, Christian.
other author:	Raidl, Gunther R.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xvi, 157 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Heuristic programming. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-30883-8
ISBN:	9783319308838

Hybrid metaheuristics = 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:

698997
Heuristic programming.

LC Class. No.: T57.84

Dewey Class. No.: 006.3

Hybrid metaheuristics = powerful tools for optimization /
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505 0 $a 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.
520 $a 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.
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