Springer Metaheuristics for Machine Learning - New Advances and Tools

Metaheuristics for Machine Learning: New Advances and Tools is a comprehensive guide from Springer that explores cutting-edge optimization techniques applied to machine learning problems. This volume, part of the Computational Intelligence Methods and Applications series, presents recent developments in metaheuristic algorithms and their practical applications across various machine learning domains. The book bridges the gap between theoretical foundations and real-world implementations, offering readers both established methods and emerging tools for solving complex optimisation challenges. Whether you are a researcher, practitioner, or student in computer science and artificial intelligence, this resource provides essential insights into how metaheuristic approaches can enhance machine learning model development, feature selection, hyperparameter tuning, and algorithm design. The compilation of new advances ensures you stay current with the latest techniques shaping the field.