Advanced Machine Learning With Evolutionary And Metaheuristic Techniques

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 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 can be helpful for research from the evolutionary computation, artificial intelligence communities.

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.

This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.

This book presents a collection of the most recent hybrid methods for image processing. The algorithms included consider evolutionary, swarm, machine learning and deep learning. The respective chapters explore different areas of image processing, from image segmentation to the recognition of objects using complex approaches and medical applications. The book also discusses the theory of the methodologies used to provide an overview of the applications of these tools in image processing. The book is primarily intended for undergraduate and postgraduate students of science, engineering and computational mathematics, and can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence. Further, it is a valuable resource for researchers from the evolutionary computation, artificial intelligence and image processing communities.

Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing Metaheuristics techniques with Machine Learning for solving biomedical problems. The book is organized to present a stepwise progression beginning with the basics of Metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of the book presents the fundamental concepts of Metaheuristics and Machine Learning, and also provides a comprehensive taxonomic view of Metaheuristics methods according to a variety of criteria such as data type, scope, method, and so forth. The second section of the book explains how to apply Metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in Metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in Metaheuristics for biomedical science. The book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice. Introduces a new set of Metaheuristics techniques for biomedical applications Presents basic concepts of Metaheuristics, methods and practices, followed by advanced topics and applications Provides researchers, practitioners, and project stakeholders with a complete guide for understanding and applying metaheuristics and machine learning techniques in their projects and solutions

METAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.