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010   $a3-540-49176-7
024 7 $a10.1007/3-540-59046-3
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035   $a(PQKBTitleCode)TC0000323006
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035   $a(DE-He213)978-3-540-49176-7
035   $a(PPN)155185586
035   $a(EXLCZ)991000000000234242
100   $a20121227d1995      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aEvolution and Biocomputation$b[electronic resource] $eComputational Models of Evolution /$fedited by Wolfgang Banzhaf, Frank H. Eckman
205   $a1st ed. 1995.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1995.
215   $a1 online resource (VIII, 284 p.) 
225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v899
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-540-59046-3 
327   $aEditors' introduction -- Aspects of optimality behavior in population genetics theory -- Optimization as a technique for studying population genetics equations -- Emergence of mutualism -- Three illustrations of artificial life's working hypothesis -- Self-organizing algorithms derived from RNA interactions -- Modeling the connection between development and evolution: Preliminary report -- Soft genetic operators in Evolutionary Algorithms -- Analysis of selection, mutation and recombination in genetic algorithms -- The role of mate choice in biocomputation: Sexual selection as a process of search, optimization, and diversification -- Genome growth and the evolution of the genotype-phenotype map.
330   $aThis volume comprises ten thoroughly refereed and revised full papers originating from an interdisciplinary workshop on biocomputation entitled "Evolution as a Computational Process", held in Monterey, California in July 1992. This book is devoted to viewing biological evolution as a giant computational process being carried out over a vast spatial and temporal scale. Computer scientists, mathematicians and physicists may learn about optimization from looking at natural evolution and biologists may learn about evolution from studying artificial life, game theory, and mathematical optimization. In addition to the ten full papers addressing e.g. population genetics, emergence, artificial life, self-organization, evolutionary algorithms, and selection, there is an introductory survey and a subject index.
410  0$aLecture Notes in Computer Science,$x0302-9743 ;$v899
606   $aEvolutionary biology
606   $aComputers
606   $aAlgorithms
606   $aArtificial intelligence
606   $aCombinatorics
606   $aBiomathematics
606   $aEvolutionary Biology$3https://scigraph.springernature.com/ontologies/product-market-codes/L21001
606   $aTheory of Computation$3https://scigraph.springernature.com/ontologies/product-market-codes/I16005
606   $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aCombinatorics$3https://scigraph.springernature.com/ontologies/product-market-codes/M29010
606   $aMathematical and Computational Biology$3https://scigraph.springernature.com/ontologies/product-market-codes/M31000
615  0$aEvolutionary biology.
615  0$aComputers.
615  0$aAlgorithms.
615  0$aArtificial intelligence.
615  0$aCombinatorics.
615  0$aBiomathematics.
615 14$aEvolutionary Biology.
615 24$aTheory of Computation.
615 24$aAlgorithm Analysis and Problem Complexity.
615 24$aArtificial Intelligence.
615 24$aCombinatorics.
615 24$aMathematical and Computational Biology.
676   $a575.1/5/015118
702   $aBanzhaf$b Wolfgang$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aEckman$b Frank H$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a996466099203316
996   $aEvolution and biocomputation$91501996
997   $aUNISA