Evolutionary Learning: Advances in Theories and Algorithms / by Zhi-Hua Zhou, Yang Yu, Chao Qian.

Format:

Book

Author/Creator:

Zhou, Zhi-Hua (Computer scientist), author.

Yu, Yang, author.

Qian, Chao, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Algorithms.

Computer science--Mathematics.

Computer science.

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Math Applications in Computer Science.

Local Subjects:

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Math Applications in Computer Science.

Physical Description:

1 online resource (XII, 361 pages) : 59 illustrations, 20 illustrations in color

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Singapore : Springer Singapore : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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. .

Contents:

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. .

Other Format:

Printed edition:

ISBN:

978-981-13-5956-9

9789811359569

9789811359552

9789811359576

Access Restriction:

Restricted for use by site license.