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

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.

 

Combined Optimization Algorithms for Incorporating DG in Distribution Systems.- Intelligent computational models for cancer diagnosis: A Comprehensive Review.- Elitist-Ant System metaheuristic for ITC 2021- Sports Timetabling.- Swarm intelligence algorithms-based Machine Learning Framework for Medical Diagnosis: A Comprehensive Review.- Aggregation of Semantically Similar News Articles with the help of Embedding Techniques and Unsupervised Machine Learning Algorithms: A Machine Learning Application with Semantic Technologies.- Integration of Machine Learning and Optimization Techniques for Cardiac Health Recognition.- Metaheuristics for Parameter Estimation of Solar Photovoltaic Cells: A Comprehensive Review.- Big Data Analysis using Hybrid Meta-heuristic Optimization Algorithm and MapReduce Framework.- Deep NeuralÃ-