Sarker / Mohammadian | Evolutionary Optimization | Buch | 978-0-7923-7654-5 | sack.de

Buch, Englisch, Band 48, 418 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 1730 g

Reihe: International Series in Operations Research & Management Science

Sarker / Mohammadian

Evolutionary Optimization

Buch, Englisch, Band 48, 418 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 1730 g

Reihe: International Series in Operations Research & Management Science

ISBN: 978-0-7923-7654-5
Verlag: Springer


Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.
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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.


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