國立虎尾科技大學 |

Evolutionary Learning: Advances in Theories and Algorithms

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Evolutionary Learning: Advances in Theories and Algorithms/ by Zhi-Hua Zhou, Yang Yu, Chao Qian.
作者:	Zhou, Zhi-Hua.
其他作者:	Yu, Yang.
面頁冊數:	XII, 361 p. 59 illus., 20 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-981-13-5956-9
ISBN:	9789811359569

Evolutionary Learning: Advances in Theories and Algorithms
Zhou, Zhi-Hua.

Evolutionary Learning: Advances in Theories and Algorithms[electronic resource] /by Zhi-Hua Zhou, Yang Yu, Chao Qian. - 1st ed. 2019. - XII, 361 p. 59 illus., 20 illus. in color.online resource.

1.Introduction -- 2. Preliminaries -- 3. Running Time Analysis: Convergence-based Analysis -- 4. Running Time Analysis: Switch Analysis -- 5. Running Time Analysis: Comparison and Unification -- 6. Approximation Analysis: SEIP -- 7. Boundary Problems of EAs -- 8. Recombination -- 9. Representation -- 10. Inaccurate Fitness Evaluation -- 11. Population -- 12. Constrained Optimization -- 13. Selective Ensemble -- 14. Subset Selection -- 15. Subset Selection: k-Submodular Maximization -- 16. Subset Selection: Ratio Minimization -- 17. Subset Selection: Noise -- 18. Subset Selection: Acceleration. .

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance. .

ISBN: 9789811359569

Standard No.: 10.1007/978-981-13-5956-9doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Evolutionary Learning: Advances in Theories and Algorithms
LDR:03414nam a22003855i 4500 001 1004420
003 DE-He213
005 20200630013415.0
007 cr nn 008mamaa
008 210106s2019 si | s |||| 0|eng d
020 $a 9789811359569 $9 978-981-13-5956-9
024 7 $a 10.1007/978-981-13-5956-9 $2 doi
035 $a 978-981-13-5956-9
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
100 1 $a Zhou, Zhi-Hua. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 683535
245 1 0 $a Evolutionary Learning: Advances in Theories and Algorithms $h [electronic resource] / $c by Zhi-Hua Zhou, Yang Yu, Chao Qian.
250 $a 1st ed. 2019.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a XII, 361 p. 59 illus., 20 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
505 0 $a 1.Introduction -- 2. Preliminaries -- 3. Running Time Analysis: Convergence-based Analysis -- 4. Running Time Analysis: Switch Analysis -- 5. Running Time Analysis: Comparison and Unification -- 6. Approximation Analysis: SEIP -- 7. Boundary Problems of EAs -- 8. Recombination -- 9. Representation -- 10. Inaccurate Fitness Evaluation -- 11. Population -- 12. Constrained Optimization -- 13. Selective Ensemble -- 14. Subset Selection -- 15. Subset Selection: k-Submodular Maximization -- 16. Subset Selection: Ratio Minimization -- 17. Subset Selection: Noise -- 18. Subset Selection: Acceleration. .
520 $a Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance. .
650 0 $a Artificial intelligence. $3 559380
650 0 $a Algorithms. $3 527865
650 0 $a Computer science—Mathematics. $3 1253519
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 593923
650 2 4 $a Math Applications in Computer Science. $3 669887
700 1 $a Yu, Yang. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 845414
700 1 $a Qian, Chao. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1226829
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811359552
776 0 8 $i Printed edition: $z 9789811359576
856 4 0 $u https://doi.org/10.1007/978-981-13-5956-9
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)