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Evolutionary Learning: Advances in Theories and Algorithms (Hardcover, 1st ed. 2019) Loot Price: R4,151
Discovery Miles 41 510
Evolutionary Learning: Advances in Theories and Algorithms (Hardcover, 1st ed. 2019): Zhi-Hua Zhou, Yang Yu, Chao Qian

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

Zhi-Hua Zhou, Yang Yu, Chao Qian

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Loot Price R4,151 Discovery Miles 41 510 | Repayment Terms: R389 pm x 12*

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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.

General

Imprint: Springer Verlag, Singapore
Country of origin: Singapore
Release date: June 2019
First published: 2019
Authors: Zhi-Hua Zhou • Yang Yu • Chao Qian
Dimensions: 235 x 155 x 27mm (L x W x T)
Format: Hardcover
Pages: 361
Edition: 1st ed. 2019
ISBN-13: 978-981-13-5955-2
Categories: Books > Computing & IT > General theory of computing > Mathematical theory of computation
Books > Computing & IT > General theory of computing > Data structures
Books > Computing & IT > Computer programming > Algorithms & procedures
Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers
Books > Computing & IT > Applications of computing > Artificial intelligence > General
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LSN: 981-13-5955-5
Barcode: 9789811359552

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