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020 _a9783030047351
_9978-3-030-04735-1
024 7 _a10.1007/978-3-030-04735-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aGenetic Programming Theory and Practice XVI
_h[electronic resource] /
_cedited by Wolfgang Banzhaf, Lee Spector, Leigh Sheneman.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXXI, 234 p. 65 illus., 47 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aGenetic and Evolutionary Computation,
_x1932-0175
505 0 _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.
520 _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.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 0 _aAlgorithms.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputational Intelligence.
650 2 4 _aAlgorithms.
700 1 _aBanzhaf, Wolfgang.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSpector, Lee.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSheneman, Leigh.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030047344
776 0 8 _iPrinted edition:
_z9783030047368
830 0 _aGenetic and Evolutionary Computation,
_x1932-0175
856 4 0 _uhttps://doi.org/10.1007/978-3-030-04735-1
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c174188
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