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010   $a3-030-04735-0
024 7 $a10.1007/978-3-030-04735-1
035   $a(CKB)4100000007522491
035   $a(DE-He213)978-3-030-04735-1
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035   $a(PPN)233798900
035   $a(EXLCZ)994100000007522491
100   $a20190123d2019      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aGenetic Programming Theory and Practice XVI /$fedited by Wolfgang Banzhaf, Lee Spector, Leigh Sheneman
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (XXI, 234 p. 65 illus., 47 illus. in color.)
225 1 $aGenetic and Evolutionary Computation,$x1932-0167
311   $a3-030-04734-2 
327   $a1 Exploring Genetic Programming Systems with MAP-Elites -- 2 The Evolutionary Buffet Method -- 3 Emergent Policy Discovery for Visual Reinforcement Learning through Tangled Program Graphs: A Tutorial -- 4 Strong Typing, Swarm Enhancement, and Deep Learning Feature Selection in the Pursuit of Symbolic Regression-Classification -- 5 Cluster Analysis of a Symbolic Regression Search Space -- 6 What else is in an evolved name? Exploring evolvable specificity with SignalGP -- Lexicase Selection Beyond Genetic Programming -- 8 Evolving developmental programs that build neural networks for solving multiple problems -- 9 The Elephant in the Room - Towards the Application of Genetic Programming to Automatic Programming -- 10 Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal -- 11 Program Search for Machine Learning Pipelines Leveraging Symbolic Planning and Reinforcement Learning.
330   $aThese contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evolving developmental programs for neural networks solving multiple problems, tangled program, transfer learning and outlier detection using GP, program search for machine learning pipelines in reinforcement learning, automatic programming with GP, new variants of GP, like SignalGP, variants of lexicase selection, and symbolic regression and classification techniques. The volume includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.
410  0$aGenetic and Evolutionary Computation,$x1932-0167
606   $aArtificial intelligence
606   $aComputational intelligence
606   $aAlgorithms
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021
615  0$aArtificial intelligence.
615  0$aComputational intelligence.
615  0$aAlgorithms.
615 14$aArtificial Intelligence.
615 24$aComputational Intelligence.
615 24$aAlgorithm Analysis and Problem Complexity.
676   $a006.3
676   $a006.31
702   $aBanzhaf$b Wolfgang$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aSpector$b Lee$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aSheneman$b Leigh$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910337578703321
996   $aGenetic Programming Theory and Practice XVI$92512319
997   $aUNINA