大葉大學圖書館 |

Genetic programming theory and practice XII[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.3823
書名/作者:	Genetic programming theory and practice XII/ edited by Rick Riolo, William P. Worzel, Mark Kotanchek.
其他題名:	Genetic programming theory and practice 12
其他作者:	Riolo, Rick.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xii, 182 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Genetic programming (Computer science) - Congresses.
標題:	Artificial intelligence
標題:	Computer Science.
標題:	Artificial Intelligence (incl. Robotics).
標題:	Algorithm Analysis and Problem Complexity.
標題:	Programming Techniques.
ISBN:	9783319160306 (electronic bk.)
ISBN:	9783319160290 (paper)
內容註:	Application 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.
摘要、提要註:	These 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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-16030-6

Genetic programming theory and practice XII[electronic resource] /
Genetic programming theory and practice XII[electronic resource] /Genetic programming theory and practice 12edited by Rick Riolo, William P. Worzel, Mark Kotanchek. - Cham :Springer International Publishing :2015. - xii, 182 p. :ill., digital ;24 cm. - Genetic and evolutionary computation,1932-0167. - Genetic and evolutionary computation..

Application 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.

These 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.

ISBN: 9783319160306 (electronic bk.)

Standard No.: 10.1007/978-3-319-16030-6doiSubjects--Topical Terms:

468983
Genetic programming (Computer science)
--Congresses.

LC Class. No.: QA76.623

Dewey Class. No.: 006.3823

Genetic programming theory and practice XII[electronic resource] /
LDR:02798nam a2200349 a 4500 001 439254
003 DE-He213
005 20160115105834.0
006 m d
007 cr nn 008maaau
008 160322s2015 gw s 0 eng d
020 $a 9783319160306 (electronic bk.)
020 $a 9783319160290 (paper)
024 7 $a 10.1007/978-3-319-16030-6 $2 doi
035 $a 978-3-319-16030-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.623
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.3823 $2 23
090 $a QA76.623 $b .G328 2015
245 0 0 $a Genetic programming theory and practice XII $h [electronic resource] / $c edited by Rick Riolo, William P. Worzel, Mark Kotanchek.
246 3 $a Genetic programming theory and practice 12
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xii, 182 p. : $b ill., digital ; $c 24 cm.
490 1 $a Genetic and evolutionary computation, $x 1932-0167
505 0 $a Application 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 $a These 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 $a Genetic programming (Computer science) $v Congresses. $3 468983
650 0 $a Artificial intelligence $3 135215
650 1 4 $a Computer Science. $3 423143
650 2 4 $a Artificial Intelligence (incl. Robotics). $3 466995
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 463701
650 2 4 $a Programming Techniques. $3 466907
700 1 $a Riolo, Rick. $3 627011
700 1 $a Worzel, William P. $3 627012
700 1 $a Kotanchek, Mark. $3 627013
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Genetic and evolutionary computation. $3 627014
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-16030-6
950 $a Computer Science (Springer-11645)