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Evolutionary learningadvances in the...

Qian, Chao.

Evolutionary learningadvances in theories and algorithms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evolutionary learningby Zhi-Hua Zhou, Yang Yu, Chao Qian.
Reminder of title:	advances in theories and algorithms /
Author:	Zhou, Zhi-Hua.
other author:	Yu, Yang.
Published:	Singapore :Springer Singapore :2019.
Description:	xii, 361 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-981-13-5956-9
ISBN:	9789811359569$q(electronic bk.)

Evolutionary learningadvances in theories and algorithms /
Zhou, Zhi-Hua.

Evolutionary learningadvances in theories and algorithms /[electronic resource] :by Zhi-Hua Zhou, Yang Yu, Chao Qian. - Singapore :Springer Singapore :2019. - xii, 361 p. :ill., digital ;24 cm.

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$q(electronic bk.)

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

188639
Machine learning.

LC Class. No.: Q325.5 / .Z46 2019

Dewey Class. No.: 006.31

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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.
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