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020			_a9789811681134
024	7		_a10.1007/978-981-16-8113-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.623 _b2022 EB
245	1	0	_aGenetic Programming Theory and Practice XVIII _cedited by Wolfgang Banzhaf, Leonardo Trujillo, Stephan Winkler, Bill Worzel
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XIV, 212 páginas) _b74 ilustraciones, 62 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSpringer_Computer_2022
650		7	_2embne _9469951 _aProgramación genética (Informática)
700	1		_aBanzhaf, Wolfgang _eeditor literario _0(orcid)0000-0002-6382-3245 _1https://orcid.org/0000-0002-6382-3245 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aTrujillo, Leonardo _eeditor literario _0(orcid)0000-0003-1812-5736 _1https://orcid.org/0000-0003-1812-5736 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aWinkler, Stephan _eeditor literario _0(orcid)0000-0002-5196-4294 _1https://orcid.org/0000-0002-5196-4294 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aWorzel, Bill _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789811681127
776	0	8	_iPrinted edition: _z9789811681141
776	0	8	_iPrinted edition: _z9789811681158
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-8113-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2022 _dz _eh _zSI