Metaheuristics for Machine Learning 1st Edition

Metaheuristics for Machine Learning — 1st Edition by Kanak Kalita, Narayanan Ganesh, and S. Balamurugan is an essential guide for practitioners and students who want to harness nature-inspired optimization to boost machine learning performance. Clear, contemporary, and practice-oriented, this volume connects core metaheuristic techniques with real-world ML challenges.

Begin exploring a compelling mix of theory and application: from evolutionary algorithms and swarm intelligence to simulated annealing and hybrid methods. Each chapter breaks down algorithmic principles, offers intuitive pseudo-code, and demonstrates how metaheuristics solve tasks such as feature selection, hyperparameter tuning, clustering, and combinatorial optimization. Comparative analyses and experimental insights help readers choose and tailor algorithms for classification, regression, and large-scale data scenarios.

Designed for accessibility without sacrificing rigor, the book suits advanced undergraduates, graduate students, data scientists, and researchers in industry and academia. It’s particularly relevant for those working in AI hubs across India, Asia, Europe, and North America who need scalable, adaptive optimization strategies for complex models.

Why this book matters: it bridges the gap between abstract optimization theory and practical machine learning workflows, enabling better model generalization, faster convergence, and smarter resource use. Practical examples and clear explanations make complex concepts usable immediately in research projects and production pipelines.

Ready to transform how your models learn? Add Metaheuristics for Machine Learning to your library today and start applying proven optimization techniques that elevate model performance across domains.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.