Metaheuristics in Machine Learning: Theory and Applications (Hardcover, 1st ed. 2021)

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 967
Release date:	July 2021
First published:	2021
Editors:	Diego Oliva • Essam H. Houssein • Salvador Hinojosa
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	769
Edition:	1st ed. 2021
ISBN-13:	978-3-03-070541-1
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-070541-2
Barcode:	9783030705411

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