LEADER 04811nam 22006974a 450 001 9910809900403321 005 20230120064713.0 010 $a1-280-46221-3 010 $a9786610462216 010 $a0-306-48041-7 024 7 $a10.1007/b101816 035 $a(CKB)111087027860526 035 $a(EBL)3035890 035 $a(SSID)ssj0000151565 035 $a(PQKBManifestationID)11146914 035 $a(PQKBTitleCode)TC0000151565 035 $a(PQKBWorkID)10317507 035 $a(PQKB)11030148 035 $a(DE-He213)978-0-306-48041-6 035 $a(Au-PeEL)EBL3035890 035 $a(CaPaEBR)ebr10067260 035 $a(CaONFJC)MIL46221 035 $a(OCoLC)559313621 035 $a(Au-PeEL)EBL197656 035 $a(MiAaPQ)EBC3035890 035 $a(MiAaPQ)EBC197656 035 $a(EXLCZ)99111087027860526 100 $a20011121d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aEvolutionary optimization /$fedited by Ruhul Sarker, Masoud Mohammadian, Xin Yao 205 $a1st ed. 2002. 210 $aBoston $cKluwer Academic Publishers$dc2002 215 $a1 online resource (433 p.) 225 1 $aInternational series in operations research & management science ;$v48 300 $aDescription based upon print version of record. 311 $a0-7923-7654-4 320 $aIncludes bibliographical references and index. 327 $aConventional 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. 330 $aEvolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization. 410 0$aInternational series in operations research & management science ;$v48. 606 $aMathematical optimization 606 $aOperations research 606 $aEvolutionary programming (Computer science) 615 0$aMathematical optimization. 615 0$aOperations research. 615 0$aEvolutionary programming (Computer science) 676 $a519.3 701 $aSarker$b Ruhul A$01369423 701 $aMohammadian$b Masoud$01653468 701 $aYao$b Xin$f1962-$0642112 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910809900403321 996 $aEvolutionary optimization$94004798 997 $aUNINA