Description
Metaheuristic Procedures for Training Neural Networks presents cutting-edge optimization techniques for improving neural network training. This SPRINGER publication by Alba E. combines theoretical foundations with practical applications of metaheuristic algorithms including genetic algorithms, particle swarm optimization, and ant colony optimization.
The book addresses limitations of conventional gradient-descent methods by introducing nature-inspired and population-based optimization strategies. Readers will discover how these procedures can escape local minima, improve convergence rates, and enhance network generalization. Ideal for researchers, practitioners, and advanced students in machine learning and artificial intelligence, this text provides both algorithmic insights and real-world implementation guidance for applying metaheuristics to neural network training challenges.
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