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

Essam Halim Houssein

ISBN 10: 3030990788 / ISBN 13: 9783030990787

Editore: Springer International Publishing Jun 2022, 2022

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 512 pp. Englisch.

Codice articolo 9783030990787

Dati bibliografici

• Titolo: Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems
• Editore: Springer International Publishing Jun 2022
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3030990788
• ISBN 13: 9783030990787
• Rilegatura: Buch
• Condizione: Neu

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Riassunto

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.