000 03858nam a22005895i 4500
001 978-3-319-34223-8
003 DE-He213
005 20200421111159.0
007 cr nn 008mamaa
008 161221s2016 gw | s |||| 0|eng d
020 _a9783319342238
_9978-3-319-34223-8
024 7 _a10.1007/978-3-319-34223-8
_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
245 1 0 _aGenetic Programming Theory and Practice XIII
_h[electronic resource] /
_cedited by Rick Riolo, W.P. Worzel, Mark Kotanchek, Arthur Kordon.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXX, 262 p. 69 illus., 31 illus. in color.
_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 _aEvolving Simple Symbolic Regression Models by Multi-objective Genetic Programming -- Learning Heuristics for Mining RNA Sequence-Structure Motifs -- Kaizen Programming for Feature Construction for Classification -- GP as if You Meant It: An Exercise for Mindful Practice -- nPool: Massively Distributed Simultaneous Evolution and Cross-Validation in EC-Star -- Highly Accurate Symbolic Regression with Noisy Training Data -- Using Genetic Programming for Data Science: Lessons Learned -- The Evolution of Everything (EvE) and Genetic Programming -- Lexicase selection for program synthesis: a Diversity Analysis -- Using Graph Databases to Explore the Dynamics of Genetic Programming Runs -- Predicting Product Choice with Symbolic Regression and Classification -- Multiclass Classification Through Multidimensional Clustering -- Prime-Time: Symbolic Regression takes its place in the Real World.
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 in this volume include: multi-objective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synthesis, symbolic regression with noisy training data, graph databases, and multidimensional clustering. It also covers several chapters on best practices and lesson learned from hands-on experience. Additional application areas include financial operations, genetic analysis, and predicting product choice. 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 _aAlgorithms.
650 0 _aArtificial intelligence.
650 0 _aOperations research.
650 0 _aManagement science.
650 0 _aComputational intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Intelligence.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aOperations Research, Management Science.
700 1 _aRiolo, Rick.
_eeditor.
700 1 _aWorzel, W.P.
_eeditor.
700 1 _aKotanchek, Mark.
_eeditor.
700 1 _aKordon, Arthur.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319342214
830 0 _aGenetic and Evolutionary Computation,
_x1932-0167
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-34223-8
912 _aZDB-2-SCS
942 _cEBK
999 _c53741
_d53741