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Evolutionary Learning: Advances in Theories and Algorithms by Zhou, Zhi-Hua; Yu, Yang; Qian, Chao is a scholarly article available to read on EtoBox.

What is Evolutionary Learning: Advances in Theories and Algorithms about?

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 opti

Author

Zhou, Zhi-Hua; Yu, Yang; Qian, Chao

Publisher

Springer Singapore : Imprint : Springer

Published

2019

Language

EN

ISBN

9789811359569