04694nam 22007335 450 991025499320332120200704040603.09783319308838331930883110.1007/978-3-319-30883-8(CKB)3710000000718266(DE-He213)978-3-319-30883-8(MiAaPQ)EBC4531673(PPN)194078442(EXLCZ)99371000000071826620160523d2016 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierHybrid Metaheuristics Powerful Tools for Optimization /by Christian Blum, Günther R. Raidl1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (XVI, 157 p. 20 illus., 9 illus. in color.)Artificial Intelligence: Foundations, Theory, and Algorithms,2365-30519783319308821 3319308823 Includes bibliographical references.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.Artificial Intelligence: Foundations, Theory, and Algorithms,2365-3051Artificial intelligenceComputersComputational intelligenceOperations researchDecision makingMathematical optimizationArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Theory of Computationhttps://scigraph.springernature.com/ontologies/product-market-codes/I16005Computational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Operations Research/Decision Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/521000Optimizationhttps://scigraph.springernature.com/ontologies/product-market-codes/M26008Artificial intelligence.Computers.Computational intelligence.Operations research.Decision making.Mathematical optimization.Artificial Intelligence.Theory of Computation.Computational Intelligence.Operations Research/Decision Theory.Optimization.005.1Blum Christianauthttp://id.loc.gov/vocabulary/relators/aut929211Raidl Günther Rauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910254993203321Hybrid Metaheuristics2088416UNINA