Evolutionary Optimization

Inbunden, Engelska, 2002

Del i serien International Series in Operations Research & Management Science

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Beskrivning

Over the years, the use and application of evolutionary computation techniques has improved resulting in a set of computational intelligence (also known as modern heuristics) tools that are particularly adept for solving complex optimization problems. Moreover, they are characteristically more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. Hence, evolutionary computation techniques have dealt with complex optimization problems better than traditional optimization techniques although they can be applied to easy and simple problems where conventional techniques work well. This volume reviews evolutionary computation techniques and surveys the most recent developments in their use for solving complex OR/MS problems.

Produktinformation

Utgivningsdatum:2002-01-31
Mått:155 x 235 x 24 mm
Vikt:785 g
Format:Inbunden
Språk:Engelska
Serie:International Series in Operations Research & Management Science
Antal sidor:418
Upplaga:2002
Förlag:Kluwer Academic Publishers
ISBN:9780792376545

Utforska kategorier

Optimering inom Naturvetenskap och teknik
Företagsekonomi inom Ekonomi och Ledarskap

Recensioner i media

From the reviews: "The book contains 17 chapters written by leading experts in evolutionary computation. ... Of special value is the analysis of evolutionary algorithms on pseudo-Boolean functions, given by Ingo Wegener. He and his coauthors are the first, who proved substantially sharp results on the expected run time and the success probability for evolutionary algorithms with (respectively without) crossover, giving sharp upper and lower bounds." (Hartmut Noltemeier, Zentralblatt MATH, Vol. 1072 (23), 2005)

Innehållsförteckning

Conventional Optimization Techniques.- Evolutionary Computation.- Single Objective Optimization.- Evolutionary Algorithms and Constrained Optimization.- Constrained Evolutionary Optimization.- Multi-Objective Optimization.- Evolutionary Multi-Objective Optimization: A Critical Review.- Multi-Objective Evolutionary Algorithms for Engineering Shape Design.- Assessment Methodologies for Multiobjective Evolutionary Algorithms.- Hybrid Algorithms.- Utilizing Hybrid Genetic Algorithms.- Using Evolutionary Algorithms to Solve Problems by Combining Choices of Heuristics.- Constrained Genetic Algorithms and Their Applications in Nonlinear Constrained Optimization.- Parameter Selection in EAs.- Parameter Selection.- Application of EAs to Practical Problems.- Design of Production Facilities Using Evolutionary Computing.- Virtual Population and Acceleration Techniques for Evolutionary Power Flow Calculation in Power Systems.- Application of EAs to Theoretical Problems.- Methods for the Analysis of Evolutionary Algorithms on Pseudo-Boolean Functions.- A Genetic Algorithm Heuristic for Finite Horizon Partially Observed Markov Decision Problems.- Using Genetic Algorithms to Find Good K-Tree Subgraphs.