國立高雄大學圖資館 |

登入

回首頁

Genetic programming theory and practice XVIII

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Genetic programming theory and practice XVIIIedited by Wolfgang Banzhaf ... [et al.].
其他題名:	Genetic programming theory and practice 18
其他作者:	Banzhaf, Wolfgang.
出版者:	Singapore :Springer Singapore :2022.
面頁冊數:	xiv, 212 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Genetic programming (Computer science)
電子資源:	https://doi.org/10.1007/978-981-16-8113-4
ISBN:	9789811681134$q(electronic bk.)

Genetic programming theory and practice XVIII
Genetic programming theory and practice XVIII[electronic resource] /Genetic programming theory and practice 18edited by Wolfgang Banzhaf ... [et al.]. - Singapore :Springer Singapore :2022. - xiv, 212 p. :ill., digital ;24 cm. - Genetic and evolutionary computation,1932-0175. - Genetic and evolutionary computation..

Chapter 1. Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs -- Chapter 2. Grammar-based Vectorial Genetic Programming for Symbolic Regression -- Chapter 3. Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming -- Chapter 4. What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms? -- Chapter 5. An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality -- Chapter 6. Feature Discovery with Deep Learning Algebra Networks -- Chapter 7. Back To The Future - Revisiting OrdinalGP & Trustable Models After a Decade -- Chapter 8. Fitness First -- Chapter 9. Designing Multiple ANNs with Evolutionary Development: Activity Dependence -- Chapter 10. Evolving and Analyzing modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules) -- Chapter 11. Evolution of the Semiconductor Industry, and the Start of X Law.

This book, written by the foremost international researchers and practitioners of genetic programming (GP), explores the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this year's edition, the topics covered include many of the most important issues and research questions in the ﬁeld, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efﬁcient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms. The book includes several chapters on best practices and lessons learned from hands-on experience. 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: 9789811681134$q(electronic bk.)

Standard No.: 10.1007/978-981-16-8113-4doiSubjects--Topical Terms:

206263
Genetic programming (Computer science)

LC Class. No.: QA76.623 / .G45 2022

Dewey Class. No.: 006.3823

Genetic programming theory and practice XVIII
LDR:03003nmm a2200349 a 4500 001 622315
003 DE-He213
005 20220215074104.0
006 m d
007 cr nn 008maaau
008 221209s2022 si s 0 eng d
020 $a 9789811681134$q(electronic bk.)
020 $a 9789811681127$q(paper)
024 7 $a 10.1007/978-981-16-8113-4 $2 doi
035 $a 978-981-16-8113-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.623 $b .G45 2022
072 7 $a UYA $2 bicssc
072 7 $a COM014000 $2 bisacsh
072 7 $a UYA $2 thema
082 0 4 $a 006.3823 $2 23
090 $a QA76.623 $b .G328 2022
245 0 0 $a Genetic programming theory and practice XVIII $h [electronic resource] / $c edited by Wolfgang Banzhaf ... [et al.].
246 3 $a Genetic programming theory and practice 18
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2022.
300 $a xiv, 212 p. : $b ill., digital ; $c 24 cm.
490 1 $a Genetic and evolutionary computation, $x 1932-0175
505 0 $a Chapter 1. Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs -- Chapter 2. Grammar-based Vectorial Genetic Programming for Symbolic Regression -- Chapter 3. Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming -- Chapter 4. What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms? -- Chapter 5. An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality -- Chapter 6. Feature Discovery with Deep Learning Algebra Networks -- Chapter 7. Back To The Future - Revisiting OrdinalGP & Trustable Models After a Decade -- Chapter 8. Fitness First -- Chapter 9. Designing Multiple ANNs with Evolutionary Development: Activity Dependence -- Chapter 10. Evolving and Analyzing modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules) -- Chapter 11. Evolution of the Semiconductor Industry, and the Start of X Law.
520 $a This book, written by the foremost international researchers and practitioners of genetic programming (GP), explores the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this year's edition, the topics covered include many of the most important issues and research questions in the ﬁeld, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efﬁcient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms. The book includes several chapters on best practices and lessons learned from hands-on experience. 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) $3 206263
650 1 4 $a Models of Computation. $3 915069
650 2 4 $a Bioinspired Technologies. $3 924135
650 2 4 $a Algorithms. $3 184661
700 1 $a Banzhaf, Wolfgang. $3 467016
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Genetic and evolutionary computation. $3 680244
856 4 0 $u https://doi.org/10.1007/978-981-16-8113-4
950 $a Computer Science (SpringerNature-11645)