Book release—Handbook of Evolutionary Machine Learning
The Centre for Data Science and Artificial Intelligence (CDSAI) proudly announces the release of the ‘Handbook of Evolutionary Machine Learning’.
This book was edited by the CDSAI Director Professor Mengjie Zhang, along with Wolfgang Banzhaf, and Penousal Machado. This collaborative effort also includes contributions from staff associated with the Centre—Dr Qi Chen, Professor Bing Xue, and Dr Bach Nguyen.
The Handbook of Evolutionary Machine Learning was developed by leading international researchers who specialise in evolutionary approaches to machine learning. Offering an in-depth exploration, the book examines various methodologies that harness evolution computing to address machine learning challenges.
“I’m delighted to witness the release of this invaluable handbook—a collection of cutting-edge methods in evolutionary machine learning. It highlights the path to advancements in AI that resonate in our daily lives, benefiting both research communities and industry alike,” says Professor Zhang.
Organised into five distinct parts, this handbook covers a wide array of essential topics— from fundamental concepts that explain the evolutionary approaches across various learning classes utilised in machine learning to evolutionary computation as a machine learning technique, detailing methodological enhancements for clustering, classification, regression, and ensemble learning.
Importantly, the book also explores the synergy between evolution and neural networks, including connections to deep learning, generative and adversarial models, and the untapped potential of evolution within large language models. Furthermore, the book delves into the integration of evolutionary computation to bolster machine learning methods, encompassing advancements in data preparation, model parametrisation, design, and validation.
The handbook finishes with a spotlight on diverse applications spanning medicine, robotics, finance, and beyond. Providing insightful reviews and showcasing real-world applications, readers gain invaluable perspectives on evolutionary machine learning's extensive applications.
The Handbook of Evolutionary Machine Learning is an indispensable reference for researchers, postgraduate students, industry practitioners, and anyone captivated by evolutionary approaches to machine learning.
More information: https://link.springer.com/book/10.1007/978-981-99-3814-8