000 03707nam a22003975i 4500
001 289032
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008 150903s2011 xxu| o |||| 0|eng d
020 _a9781461417705
_99781461417705
024 7 _a10.1007/9781461417705
_2doi
035 _avtls000340676
039 9 _a201509030350
_bVLOAD
_c201404300418
_dVLOAD
_y201402061031
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
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.
300 _axxviii, 264 páginas
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aGenetic and Evolutionary Computation,
_x1932-0167
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
700 1 _aMoore, Jason H.
_eeditor.
_9319062
710 2 _aSpringerLink (Servicio en línea)
_9299170
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)
942 _c14
999 _c289032
_d289032