UID:
almahu_9949198416702882
Format:
XIV, 418 p.
,
online resource.
Edition:
1st ed. 2002.
ISBN:
9780306480416
Series Statement:
International Series in Operations Research & Management Science, 48
Content:
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.
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9781475775709
Additional Edition:
Printed edition: ISBN 9780792376545
Additional Edition:
Printed edition: ISBN 9781475775693
Language:
English
URL:
https://doi.org/10.1007/b101816
