Evolutionary Learning: Advances in Theories and Algorithms

Evolutionary Learning: Advances in Theories and Algorithms Front Cover
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361 pages

Book Description

Many tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective ; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary to address optimization problems in , 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 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 of running time and approximation 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 .

Book Details

  • Title: Evolutionary Learning: Advances in Theories and Algorithms
  • Author: , ,
  • Length: 361 pages
  • Edition: 1st ed. 2019
  • Language: English
  • Publisher:
  • Publication Date: 2019-07-19
  • ISBN-10: 9811359555
  • ISBN-13: 9789811359552
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