Evolutionary optimization / / edited by Ruhul Sarker, Masoud Mohammadian, Xin Yao

• Record Nr.: UNINA9910809900403321
• Pubbl/distr/stampa: Boston, : Kluwer Academic Publishers, c2002
• ISBN: 1-280-46221-3; 9786610462216; 0-306-48041-7
• Edizione: [1st ed. 2002.]
• Descrizione fisica: 1 online resource (433 p.)
• Collana: International series in operations research & management science ; ; 48
• Altri autori (Persone): SarkerRuhul A; MohammadianMasoud; YaoXin <1962->
• Disciplina: 519.3
• Soggetti: Mathematical optimization; Operations research; Evolutionary programming (Computer science)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.