000 | 03908nam a22005175i 4500 | ||
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001 | u372474 | ||
003 | SIRSI | ||
005 | 20160812084100.0 | ||
007 | cr nn 008mamaa | ||
008 | 111031s2011 xxu| s |||| 0|eng d | ||
020 |
_a9781461417705 _9978-1-4614-1770-5 |
||
040 | _cMX-MeUAM | ||
050 | 4 | _aQ334-342 | |
050 | 4 | _aTJ210.2-211.495 | |
082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aRiolo, Rick. _eeditor. |
|
245 | 1 | 0 |
_aGenetic Programming Theory and Practice IX _h[recurso electrónico] / _cedited by Rick Riolo, Ekaterina Vladislavleva, Jason H. Moore. |
264 | 1 |
_aNew York, NY : _bSpringer New York, _c2011. |
|
300 |
_aXXVIII, 264 p. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aGenetic and Evolutionary Computation, _x1932-0167 |
|
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. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aInformation theory. | |
650 | 0 | _aComputer software. | |
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aTheory of Computation. |
650 | 2 | 4 | _aAlgorithm Analysis and Problem Complexity. |
650 | 2 | 4 | _aProgramming Techniques. |
700 | 1 |
_aVladislavleva, Ekaterina. _eeditor. |
|
700 | 1 |
_aMoore, Jason H. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9781461417699 |
830 | 0 |
_aGenetic and Evolutionary Computation, _x1932-0167 |
|
856 | 4 | 0 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-4614-1770-5 |
596 | _a19 | ||
942 | _cLIBRO_ELEC | ||
999 |
_c200354 _d200354 |