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010   $a3-319-41192-6
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035   $a(EXLCZ)993710000000765140
100   $a20160720d2016      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aSearch and Optimization by Metaheuristics $eTechniques and Algorithms Inspired by Nature /$fby Ke-Lin Du, M. N. S. Swamy
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Birkhäuser,$d2016.
215   $a1 online resource (XXI, 434 p. 68 illus., 40 illus. in color.)
311 08$a3-319-41191-8 
320   $aIncludes bibliographical references and index.
327   $aPreface -- Introduction -- Simulated Annealing -- Optimization by Recurrent Neural Networks -- Genetic Algorithms and Genetic Programming -- Evolutionary Strategies -- Differential Evolution -- Estimation of Distribution Algorithms -- Mimetic Algorithms -- Topics in EAs -- Particle Swarm Optimization -- Artificial Immune Systems -- Ant Colony Optimization -- Tabu Search and Scatter Search -- Bee Metaheuristics -- Harmony Search -- Biomolecular Computing -- Quantum Computing -- Other Heuristics-Inspired Optimization Methods -- Dynamic, Multimodal, and Constraint-Satisfaction Optimizations -- Multiobjective Optimization -- Appendix 1: Discrete Benchmark Functions -- Appendix 2: Test Functions -- Index.
330   $aThis textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others. General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described. Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics. Introduced in the appendix are some benchmarks for the evaluation of metaheuristics. Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science. It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.
606   $aComputer science$xMathematics
606   $aAlgorithms
606   $aMathematical optimization
606   $aComputer simulation
606   $aComputational intelligence
606   $aComputational Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/M14026
606   $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018
606   $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008
606   $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
615  0$aComputer science$xMathematics.
615  0$aAlgorithms.
615  0$aMathematical optimization.
615  0$aComputer simulation.
615  0$aComputational intelligence.
615 14$aComputational Science and Engineering.
615 24$aAlgorithms.
615 24$aOptimization.
615 24$aSimulation and Modeling.
615 24$aComputational Intelligence.
676   $a003.3
700   $aDu$b Ke-Lin$4aut$4http://id.loc.gov/vocabulary/relators/aut$0756075
702   $aSwamy$b M. N. S$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
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906   $aBOOK
912   $a9910254066703321
996   $aSearch and Optimization by Metaheuristics$92235912
997   $aUNINA