國立虎尾科技大學 |

Genetic Programming Theory and Practice XVII

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Genetic Programming Theory and Practice XVII/ edited by Wolfgang Banzhaf, Erik Goodman, Leigh Sheneman, Leonardo Trujillo, Bill Worzel.
other author:	Banzhaf, Wolfgang.
Description:	XXVI, 409 p. 142 illus., 112 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-39958-0
ISBN:	9783030399580

Genetic Programming Theory and Practice XVII
Genetic Programming Theory and Practice XVII[electronic resource] /edited by Wolfgang Banzhaf, Erik Goodman, Leigh Sheneman, Leonardo Trujillo, Bill Worzel. - 1st ed. 2020. - XXVI, 409 p. 142 illus., 112 illus. in color.online resource. - Genetic and Evolutionary Computation,1932-0167. - Genetic and Evolutionary Computation,.

1. Characterizing the Effects of Random Subsampling on Lexicase Selection -- 2. It is Time for New Perspectives on How to Fight Bloatin GP -- 3. Explorations of the Semantic Learning Machine Neuroevolution Algorithm -- 4. Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics? -- 5. Symbolic Regression by Exhaustive Search – Reducing the Search Space using Syntactical Constraints and Efﬁcient Semantic Structure Deduplication -- 6. Temporal Memory Sharing in Visual Reinforcement Learning -- 7. The Evolution of Representations in Genetic Programming Trees -- 8. How Competitive is Genetic Programming in Business Data Science Applications? -- 9. Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming -- 10. Evolutionary Computation and AI Safety -- 11. Genetic Programming Symbolic Regression -- 12. Hands-on Artiﬁcial Evolution through Brain Programming -- 13. Comparison of Linear Genome Representations For Software Synthesis -- 14. Enhanced Optimization with Composite Objectives and Novelty Pulsation -- 15. New Pathways in Coevolutionary Computation -- 16. 2019 Evolutionary Algorithms Review -- 17. Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model -- 18. Modelling Genetic Programming as a Simple Sampling Algorithm -- 19. An Evolutionary System for Better Automatic Software Repair -- Index.

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. 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 volume 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: 9783030399580

Standard No.: 10.1007/978-3-030-39958-0doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Genetic Programming Theory and Practice XVII
LDR:03756nam a22004095i 4500 001 1017406
003 DE-He213
005 20200704194948.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030399580 $9 978-3-030-39958-0
024 7 $a 10.1007/978-3-030-39958-0 $2 doi
035 $a 978-3-030-39958-0
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Genetic Programming Theory and Practice XVII $h [electronic resource] / $c edited by Wolfgang Banzhaf, Erik Goodman, Leigh Sheneman, Leonardo Trujillo, Bill Worzel.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XXVI, 409 p. 142 illus., 112 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Genetic and Evolutionary Computation, $x 1932-0167
505 0 $a 1. Characterizing the Effects of Random Subsampling on Lexicase Selection -- 2. It is Time for New Perspectives on How to Fight Bloatin GP -- 3. Explorations of the Semantic Learning Machine Neuroevolution Algorithm -- 4. Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics? -- 5. Symbolic Regression by Exhaustive Search – Reducing the Search Space using Syntactical Constraints and Efﬁcient Semantic Structure Deduplication -- 6. Temporal Memory Sharing in Visual Reinforcement Learning -- 7. The Evolution of Representations in Genetic Programming Trees -- 8. How Competitive is Genetic Programming in Business Data Science Applications? -- 9. Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming -- 10. Evolutionary Computation and AI Safety -- 11. Genetic Programming Symbolic Regression -- 12. Hands-on Artiﬁcial Evolution through Brain Programming -- 13. Comparison of Linear Genome Representations For Software Synthesis -- 14. Enhanced Optimization with Composite Objectives and Novelty Pulsation -- 15. New Pathways in Coevolutionary Computation -- 16. 2019 Evolutionary Algorithms Review -- 17. Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model -- 18. Modelling Genetic Programming as a Simple Sampling Algorithm -- 19. An Evolutionary System for Better Automatic Software Repair -- Index.
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. 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 volume 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 Artificial intelligence. $3 559380
650 0 $a Computational intelligence. $3 568984
650 0 $a Algorithms. $3 527865
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 593923
700 1 $a Banzhaf, Wolfgang. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1207083
700 1 $a Goodman, Erik. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1298009
700 1 $a Sheneman, Leigh. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1301381
700 1 $a Trujillo, Leonardo. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1290880
700 1 $a Worzel, Bill. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 677855
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030399573
776 0 8 $i Printed edition: $z 9783030399597
776 0 8 $i Printed edition: $z 9783030399603
830 0 $a Genetic and Evolutionary Computation, $x 1932-0167 $3 1261942
856 4 0 $u https://doi.org/10.1007/978-3-030-39958-0
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)