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020 _a9789811681134
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024 7 _a10.1007/978-981-16-8113-4
_2doi
050 4 _aQA75.5-76.95
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aUYA
_2thema
082 0 4 _a004.0151
_223
245 1 0 _aGenetic Programming Theory and Practice XVIII
_h[electronic resource] /
_cedited by Wolfgang Banzhaf, Leonardo Trujillo, Stephan Winkler, Bill Worzel.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aXIV, 212 p. 74 illus., 62 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 _aChapter 1. Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs -- Chapter 2. Grammar-based Vectorial Genetic Programming for Symbolic Regression -- Chapter 3. Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming -- Chapter 4. What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms? -- Chapter 5. An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality -- Chapter 6. Feature Discovery with Deep Learning Algebra Networks -- Chapter 7. Back To The Future — Revisiting OrdinalGP & Trustable Models After a Decade -- Chapter 8. Fitness First -- Chapter 9. Designing Multiple ANNs with Evolutionary Development: Activity Dependence -- Chapter 10. Evolving and Analyzing modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules) -- Chapter 11. Evolution of the Semiconductor Industry, and the Start of X Law.
520 _aThis book, written by the foremost international researchers and practitioners of genetic programming (GP), explores the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this year’s edition, the topics covered include many of the most important issues and research questions in the field, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms. The book 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 _aComputer science.
650 0 _aBionics.
650 0 _aAlgorithms.
650 1 4 _aModels of Computation.
650 2 4 _aBioinspired Technologies.
650 2 4 _aAlgorithms.
700 1 _aBanzhaf, Wolfgang.
_eeditor.
_0(orcid)
_10000-0002-6382-3245
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aTrujillo, Leonardo.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWinkler, Stephan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWorzel, Bill.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811681127
776 0 8 _iPrinted edition:
_z9789811681141
776 0 8 _iPrinted edition:
_z9789811681158
830 0 _aGenetic and Evolutionary Computation,
_x1932-0175
856 4 0 _uhttps://doi.org/10.1007/978-981-16-8113-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
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