Sarker, Ruhul, Mohammadian, Masoud, Xin Yao (Eds.)
2002, XIV, 418 p.
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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.
Preface. Contributing Authors.
Part I: Introduction. 1. Conventional Optimization Techniques; M.S. Hillier, F.S. Hillier. 2. Evolutionary Computation; Xin Yao.
Part II: Single Objective Optimization. 3. Evolutionary Algorithms and Constrained Optimization; Z. Michalewicz, M. Schmidt. 4. Constrained Evolutionary Optimization; T. Runarsson, Xin Yao.
Part III: Multi-Objective Optimization. 5. Evolutionary Multiobjective Optimization; C.A. Coello Coello. 6. MEA for Engineering Shape Design; K. Deb, T. Goel. 7. Assessment Methodologies for MEAs; R. Saker, C.A. Coello Coello.
Part IV: Hybrid Algorithms. 8. Hybrid Genetic Algorithms; J.A. Joines, M.G. Kay. 9. Combining choices of heuristics; P. Ross, E. Hart. 10. Nonlinear Constrained Optimization; B.W. Wah, Yi-Xin Chen.
Part V: Parameter Selection in EAs. 11. Parameter Selection; Z. Michalewicz, et al.
Part VI: Application of EAs to Practical Problems. 12. Design of Production Facilities. 13. Virtual Population and Acceleration Techniques.
Part VII: Application of EAs to Theoretical Problems. 14. Methods for the analysis of EAs on pseudo-boolean functions; I. Wegener. 15. A GA Heuristic For Finite Horizon POMDPs; A.Z.-Z. Lin, et al. 16. Finding Good k-Tree Subgraphs; E. Ghashghai, R.L. Rardin.