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.
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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)
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. Index.
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Hardcover. Condizione: new. Hardcover. 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. The use of evolutionary computation techniques has grown considerably. The use and applications of these techniques have been further enhanced resulting in a set of computational intelligence tools that are adept for solving complex optimization problems. This book covers the foundation as well as the practical side of evolutionary optimization. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780792376545
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