Handbook of Evolutionary Machine Learning (1st ed. 2023)

This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Genetic and Evolutionary Computation
Release date:	November 2023
First published:	2023
Editors:	Wolfgang Banzhaf • Penousal Machado • Mengjie Zhang
Dimensions:	235 x 155mm (L x W)
Pages:	780
Edition:	1st ed. 2023
ISBN-13:	978-981-9938-13-1
Categories:	Books
LSN:	981-9938-13-9
Barcode:	9789819938131

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