LEADER 03784oam 2200541Ka 450 001 9910260611103321 005 20160803134623.0 035 $a(CKB)2670000000263658 035 $a(SSID)ssj0000937612 035 $a(PQKBManifestationID)11479575 035 $a(PQKBTitleCode)TC0000937612 035 $a(PQKBWorkID)10876850 035 $a(PQKB)10467429 035 $a(WaSeSS)Ind00065579 035 $a(OCoLC)827304142 035 $a(OCoLC-P)827304142 035 $a(MaCbMITP)1109 035 $a(EXLCZ)992670000000263658 100 $a20130212d1996 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAdvances in genetic programming$hVol. 2 /$fedited by Peter J. Angeline and Kenneth E. Kinnear, Jr 210 $aCambridge, Mass. ;$aLondon $cMIT Press$dİ1996 215 $a1 online resource (xv, 538 p.) 225 1 $aComplex adaptive systems 225 0 $aA Bradford book 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-262-29079-0 320 $aIncludes bibliographical references and index. 330 $aGenetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field.Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming. The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques--adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation. Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures. The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers. 606 $aGenetic programming (Computer science) 606 $aComputer programming 610 $aCOMPUTER SCIENCE/Artificial Intelligence 610 $aCOMPUTER SCIENCE/Machine Learning & Neural Networks 615 0$aGenetic programming (Computer science) 615 0$aComputer programming. 676 $a006.3 701 $aKinnear$b Kenneth E$01252381 701 $aAngeline$b Peter J$01252382 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910260611103321 996 $aAdvances in genetic programming$92903496 997 $aUNINA