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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.
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.
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