Evolutionary learning : advances in theories and algorithms / Zhi-Hua Zhou, Yang Yu, Chao Qian.

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Evolutionary learning : advances in theories and algorithms / Zhi-Hua Zhou, Yang Yu, Chao Qian.

9789811359569 (electronic book)
9811359563 (electronic book)
9811359555
9789811359552

Singapore : Springer, [2019]

English

1 online resource

10.1007/978-981-13-5 doi

Q325.5 .Z46 2019

006.31
006.3

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.

Includes bibliographical references.

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Description based on online resource; title from digital title page (viewed on June 25, 2019).

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