Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

Mohamed Abd Elaziz, Diego Oliva, Essam Halim Houssein, Laith Abualigah

ISBN 10: 3030990818 / ISBN 13: 9783030990817

Published by Springer Nature Switzerland AG, CH, 2026

About this Item

Seller Inventory # LU-9783030990817

Bibliographic Details

• Title: Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems
• Publisher: Springer Nature Switzerland AG, CH
• Publication Date: 2026
• Language: English
• ISBN 10: 3030990818
• ISBN 13: 9783030990817
• Binding: Paperback
• Edition: 2022 ed.
• Condition: New
• Dimensions: 15.49W x 2.62H x 23.5D centimeters 15.49 centimeters width by 2.62 centimeters height by 23.5 centimeters depth
• Weight: 712 grams

About this title

Synopsis

This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. 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 can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material canbe helpful for research from the evolutionary computation, artificial intelligence communities.