Metaheuristics for Machine Learning: Algorithms and Applications

Metaheuristics for Machine Learning: Algorithms and Applications is a comprehensive guide to the intersection of nature-inspired optimization and machine learning. It offers detailed discussions of algorithmic theory, real-world case studies, and interdisciplinary knowledge, making it essential for researchers, practitioners, and students in computer science, data science, engineering, and mathematics.

Format: Hardback
Length: 352 pages
Publication date: 16 April 2024
Publisher: John Wiley & Sons Inc

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 field. The book begins by introducing the fundamental concepts of metaheuristic optimization, including the definition of optimization problems, the characteristics of metaheuristic algorithms, and the evaluation of their performance. It then delves into the various types of metaheuristic algorithms, such as evolutionary algorithms, swarm intelligence algorithms, and neural network algorithms, and explores their applications in machine learning. One of the key strengths of the book is its practical approach to metaheuristic optimization. It provides step-by-step instructions on how to implement metaheuristic algorithms in machine learning workflows, including the selection of appropriate algorithms, the tuning of algorithm parameters, and the evaluation of algorithm performance. The book also includes numerous case studies and real-world examples that illustrate the effectiveness of metaheuristic optimization in solving complex optimization problems. In addition to its practical applications, the book also discusses the theoretical foundations of metaheuristic optimization. It explores the mathematical principles behind these algorithms, including optimization algorithms, search algorithms, and heuristic search algorithms. It also discusses the challenges and limitations of metaheuristic optimization, such as the difficulty of finding the global optimum and the potential for premature convergence. Overall, Metaheuristics for Machine Learning: Algorithms and Applications is a valuable resource for anyone interested in learning about metaheuristic optimization and its applications in machine learning. Whether you are a researcher, engineer, or practitioner, this book will provide you with the knowledge and skills necessary to develop effective optimization solutions for your problems.
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 field. The book begins by introducing the fundamental concepts of metaheuristic optimization, including the definition of optimization problems, the characteristics of metaheuristic algorithms, and the evaluation of their performance. It then delves into the various types of metaheuristic algorithms, such as evolutionary algorithms, swarm intelligence algorithms, and neural network algorithms, and explores their applications in machine learning. One of the key strengths of the book is its practical approach to metaheuristic optimization. It provides step-by-step instructions on how to implement metaheuristic algorithms in machine learning workflows, including the selection of appropriate algorithms, the tuning of algorithm parameters, and the evaluation of algorithm performance. The book also includes numerous case studies and real-world examples that illustrate the effectiveness of metaheuristic optimization in solving complex optimization problems. In addition to its practical applications, the book also discusses the theoretical foundations of metaheuristic optimization. It explores the mathematical principles behind these algorithms, including optimization algorithms, search algorithms, and heuristic search algorithms. It also discusses the challenges and limitations of metaheuristic optimization, such as the difficulty of finding the global optimum and the potential for premature convergence. Overall, Metaheuristics for Machine Learning: Algorithms and Applications is a valuable resource for anyone interested in learning about metaheuristic optimization and its applications in machine learning. Whether you are a researcher, engineer, or practitioner, this book will provide you with the knowledge and skills necessary to develop effective optimization solutions for your problems.

Weight: 726g
ISBN-13: 9781394233922