This book presents the theory and applications of Genetic Algorithms (GA) for optimization. It explores binary and real-coded GAs, elitist strategies, hybrid approaches using orthogonal arrays, and constraint handling through penalty methods. It examines multi-objective optimization techniques, compares VEGA and MOGA, and applies MOGA to a fuzzy multi-objective supply chain model, demonstrating efficient solutions under uncertainty and diverse Pareto-optimal outcomes.
AmazonPages: 124, Paperback, LAP Lambert Academic Publishing
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