(Ebook) Metaheuristics for Machine Learning: Algorithms and Applications by Kanak Kalita, Narayanan Ganesh, S. Balamurugan ISBN 9781394233922, 1394233922

(Ebook) Metaheuristics for Machine Learning: Algorithms and Applications by Kanak Kalita, Narayanan Ganesh, S. Balamurugan ISBN 9781394233922, 1394233922

Metaheuristics for Machine Learning: Algorithms and ApplicationsMETAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimisation in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications.The field of metaheuristic optimisation algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behaviour, and neural systems, have shown remarkable efficiency in solving complex problems.With advancements in machine learning and artificial intelligence, the application of metaheuristic optimisation techniques has expanded, demonstrating significant potential in optimising 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 optimisation 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 optimisation 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 ca