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.

Provides representative tools used for machine learning and metaheuristic algorithms Focuses on the theory and application of metaheuristic algorithms in machine learning, including hybridization and implementations in different fields Is self-explained and explains the used algorithm, the selected problem, and the implementation Offers practical examples, comparisons, and experimental results Presents topics which are selected based on their importance and complexity in the field, for example, biochemistry, image processing, clustering, feature selection, energy, among others

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