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020 _a9783319160306
_z978-3-319-16030-6
024 7 _a10.1007/978-3-319-16030-6
_2doi
040 _aTR-AnTOB
_beng
_cTR-AnTOB
_erda
050 4 _aQ334-342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema006.3
_223
245 1 0 _aGenetic Programming Theory and Practice XII /
_cedited by Rick Riolo, William P. Worzel, Mark Kotanchek.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aGenetic and Evolutionary Computation,
_x1932-0167
505 0 _aApplication of Machine-Learing Methods to Understand Gene Expression Regulation -- Identification of Novel Genetic Models of Glaucoma using the "Emergent" Genetic Programming-Based Artificial Intelligence System -- Inheritable Epigenetics in Genetic Programming -- SKGP: The Way of the Combinator -- Sequential Symbolic Regression with Genetic Programming -- Sliding Window Symbolic Regression for Detecting Changes of System Dynamics -- Extremely Accurate Symbolic Regression for Large Feature Problems -- How to Exploit Alignment in the Error Space: Two Different GP Models -- Analyzing a Decade of Human-Competitive ("HUMIE") Winners: What Can We Learn? -- Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System.
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: gene expression regulation, novel genetic models for glaucoma, inheritable epigenetics, combinators in genetic programming, sequential symbolic regression, system dynamics, sliding window symbolic regression, large feature problems, alignment in the error space, HUMIE winners, Boolean multiplexer function, and highly distributed genetic programming systems. Application areas include chemical process control, circuit design, financial data mining and bioinformatics. 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 _aArtificial intelligence.
650 0 _aComputer software.
650 0 _aComputer science.
650 1 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aAlgorithm Analysis and Problem Complexity.
_0http://scigraph.springernature.com/things/product-market-codes/I16021
650 2 4 _aProgramming Techniques.
_0http://scigraph.springernature.com/things/product-market-codes/I14010
700 1 _aRiolo, Rick.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWorzel, William P.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKotanchek, Mark.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-3-319-16030-6
_3Springer eBooks
_zOnline access link to the resource
942 _2lcc
_cEBK
041 _aeng