Kalita K., Ganesh N., Balamurugan S. (eds.) Metaheuristics for Machine Learning: Algorithms and Applications

Wiley-Scrivener, 2024. — 327 p. — (Artificial Intelligence and Soft Computing for Industrial Transformation). — ISBN: 978-1-394-23392-2.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.Genetic algorithms (GAs) fit a cluster of metaheuristic optimization techniques that draw inspiration from natural selection and genetics. to find the optimal solution for a particular issue, the core idea underlying the GA is to mimic the evolutionary process. The genetic algorithm hascandress challenges spanning various fields such as biology, engineering, and finance. In the methodology of the GA, a potential solution is denoted as a chromosome or a collection of genes. Each gene within the context of the problem signifies an individual variable, and its value corresponds to the potential range of values that the variable can take. Subsequently, these chromosomes undergo genetic operations like mutation and crossover. This process can give rise to a fresh population of potential solutions, resulting in a novel set of potential outcomes.True PDF