LEADER 03296nam 2200553 450 001 9910817420203321 005 20240131152433.0 010 $a3-662-07418-4 024 7 $a10.1007/978-3-662-07418-3 035 $a(CKB)2660000000028407 035 $a(SSID)ssj0000934192 035 $a(PQKBManifestationID)11563324 035 $a(PQKBTitleCode)TC0000934192 035 $a(PQKBWorkID)10910091 035 $a(PQKB)11093371 035 $a(DE-He213)978-3-662-07418-3 035 $a(MiAaPQ)EBC3100463 035 $a(EXLCZ)992660000000028407 100 $a20170510h19941994 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aGenetic algorithms + data structures = evolution programs /$fZbigniew Michalewicz 205 $aSecond, Extended Edition. 210 1$aBerlin, [Germany] :$cSpringer-Verlag,$d1994. 210 4$dİ1994 215 $a1 online resource (XVI, 340 p. 26 illus.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-662-07420-6 320 $aIncludes bibliographical references and index. 327 $aI. Genetic Algorithms -- 1 GAs: What Are They? -- 2 GAs: How Do They Work? -- 3 GAs: Why Do They Work? -- 4 GAs: Selected Topics -- II. Numerical Optimization -- 5 Binary or Float? -- 6 Fine Local Tuning -- 7 Handling Constraints -- 8 Evolution Strategies and Other Methods -- III. Evolution Programs -- 9 The Transportation Problem -- 10 The Traveling Salesman Problem -- 11 Drawing Graphs, Scheduling, Partitioning, and Path Planning -- 12 Machine Learning -- Conclusions -- References. 330 $aGenetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques has been growing in the last decade, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. It is aimed at researchers, practitioners, and graduate students in computer science and artificial intelligence, operations research, and engineering. This second edition includes several new sections and many references to recent developments. A simple example of genetic code and an index are also added. Writing an evolution program for a given problem should be an enjoyable experience - this book may serve as a guide to this task. 606 $aEvolutionary programming (Computer science) 606 $aGenetic algorithms 606 $aData structures (Computer science) 615 0$aEvolutionary programming (Computer science) 615 0$aGenetic algorithms. 615 0$aData structures (Computer science) 676 $a006.3 700 $aMichalewicz$b Zbigniew$044095 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910817420203321 996 $aGenetic algorithms + data structures$9928604 997 $aUNINA