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010   $a3-662-07418-4
024 7 $a10.1007/978-3-662-07418-3
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035   $a(DE-He213)978-3-662-07418-3
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100   $a20130217d1994      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aGenetic Algorithms + Data Structures = Evolution Programs /$fby Zbigniew Michalewicz
205   $a2nd ed. 1994.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1994.
215   $a1 online resource (XVI, 340 p. 26 illus.)
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$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   $aArtificial intelligence
606   $aAlgorithms
606   $aNumerical analysis
606   $aComputer programming
606   $aSoftware engineering
606   $aOperations research
606   $aArtificial Intelligence
606   $aAlgorithms
606   $aNumerical Analysis
606   $aProgramming Techniques
606   $aSoftware Engineering
606   $aOperations Research and Decision Theory
615  0$aArtificial intelligence.
615  0$aAlgorithms.
615  0$aNumerical analysis.
615  0$aComputer programming.
615  0$aSoftware engineering.
615  0$aOperations research.
615 14$aArtificial Intelligence.
615 24$aAlgorithms.
615 24$aNumerical Analysis.
615 24$aProgramming Techniques.
615 24$aSoftware Engineering.
615 24$aOperations Research and Decision Theory.
676   $a006.3
700   $aMichalewicz$b Zbigniew$044095
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910973790503321
996   $aGenetic algorithms + data structures$9928604
997   $aUNINA