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001 | 289032 | ||
003 | MX-SnUAN | ||
005 | 20170705134218.0 | ||
007 | cr nn 008mamaa | ||
008 | 150903s2011 xxu| o |||| 0|eng d | ||
020 |
_a9781461417705 _99781461417705 |
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024 | 7 |
_a10.1007/9781461417705 _2doi |
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035 | _avtls000340676 | ||
039 | 9 |
_a201509030350 _bVLOAD _c201404300418 _dVLOAD _y201402061031 _zstaff |
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040 |
_aMX-SnUAN _bspa _cMX-SnUAN _erda |
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050 | 4 | _aQ334-342 | |
100 | 1 |
_aRiolo, Rick. _eeditor. _9299568 |
|
245 | 1 | 0 |
_aGenetic Programming Theory and Practice IX / _cedited by Rick Riolo, Ekaterina Vladislavleva, Jason H. Moore. |
264 | 1 |
_aNew York, NY : _bSpringer New York, _c2011. |
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300 |
_axxviii, 264 páginas _brecurso en línea. |
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336 |
_atexto _btxt _2rdacontent |
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337 |
_acomputadora _bc _2rdamedia |
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338 |
_arecurso en línea _bcr _2rdacarrier |
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347 |
_aarchivo de texto _bPDF _2rda |
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490 | 0 |
_aGenetic and Evolutionary Computation, _x1932-0167 |
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500 | _aSpringer eBooks | ||
505 | 0 | _aWhat’s in an evolved name? The evolution of modularity via tag-based Reference -- Let the Games Evolve! -- Novelty Search and the Problem with Objectives -- A fine-grained view of phenotypes and locality in genetic programming -- Evolution of an Effective Brain-Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control -- Improved Time Series Prediction and Symbolic Regression with Affine Arithmetic -- Computational Complexity Analysis of Genetic Programming – Initial Results and Future Directions -- Accuracy in Symbolic Regression -- Human-Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer -- Baseline Genetic Programming: Symbolic Regression on Benchmarks for Sensory Evaluation Modeling -- Detecting Shadow Economy Sizes With Symbolic Regression -- The Importance of Being Flat – Studying the Program Length Distributions of Operator Equalisation -- FFX: Fast, Scalable, Deterministic Symbolic Regression Technology. | |
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 include: modularity and scalability; evolvability; human-competitive results; the need for important high-impact GP-solvable problems;; the risks of search stagnation and of cutting off paths to solutions; the need for novelty; empowering GP search with expert knowledge; In addition, GP symbolic regression is thoroughly discussed, addressing such topics as guaranteed reproducibility of SR; validating SR results, measuring and controlling genotypic complexity; controlling phenotypic complexity; identifying, monitoring, and avoiding over-fitting; finding a comprehensive collection of SR benchmarks, comparing SR to machine learning. This text is for all GP explorers. 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. | ||
590 | _aPara consulta fuera de la UANL se requiere clave de acceso remoto. | ||
700 | 1 |
_aVladislavleva, Ekaterina. _eeditor. _9313144 |
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700 | 1 |
_aMoore, Jason H. _eeditor. _9319062 |
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710 | 2 |
_aSpringerLink (Servicio en línea) _9299170 |
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776 | 0 | 8 |
_iEdición impresa: _z9781461417699 |
856 | 4 | 0 |
_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4614-1770-5 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL) |
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_c289032 _d289032 |