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Evolutionary Learning: Advances in Theories and Algorithms (Hardcover, 1st ed. 2019)
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Evolutionary Learning: Advances in Theories and Algorithms (Hardcover, 1st ed. 2019)
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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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