Metaheuristics for Machine Learning, Algorithms and Applications

Metaheuristics for Machine Learning: Algorithms and Applications

Metaheuristics for Machine Learning: Algorithms and Applications provides a comprehensive and practical exploration of metaheuristic optimization techniques and their powerful role in modern machine learning. As machine learning models grow increasingly complex, traditional optimization methods often struggle with high-dimensional, nonlinear, and multimodal problems. This book presents metaheuristic algorithms as flexible and effective solutions to these challenges.

Covering a wide range of techniques—including Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Differential Evolution, Simulated Annealing, and other nature-inspired methods—the book explains their theoretical foundations as well as their practical implementation in machine learning tasks.

Readers will discover how metaheuristics can be applied to:

• Feature selection and dimensionality reduction

• Hyperparameter tuning and model optimization

• Neural network training

• Clustering and classification problems

• Deep learning architecture search

The book combines clear algorithmic explanations with real-world applications, case studies, and implementation insights, making it valuable for both researchers and practitioners. Mathematical concepts are presented with clarity, while practical examples demonstrate how these methods can improve performance across various domains.

Ideal for graduate students, data scientists, AI researchers, and professionals in computational intelligence, Metaheuristics for Machine Learning serves as both a reference guide and a hands-on resource for leveraging advanced optimization techniques in machine learning systems.

Size: A4(20*28cm)

Printing: 80 gm – color

Cover: Softcover