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001 978-1-4614-1770-5
003 DE-He213
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008 111031s2011 xxu| s |||| 0|eng d
020 _a9781461417705
_9978-1-4614-1770-5
024 7 _a10.1007/978-1-4614-1770-5
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aRiolo, Rick.
_eeditor.
245 1 0 _aGenetic Programming Theory and Practice IX
_h[electronic resource] /
_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
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
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 _uhttp://dx.doi.org/10.1007/978-1-4614-1770-5
912 _aZDB-2-SCS
999 _c106299
_d106299