توضیحات
This book explores the integration of evolutionary algorithms and metaheuristic methods with advanced machine learning models to solve complex optimization and prediction problems. It covers topics such as genetic algorithms, particle swarm optimization, simulated annealing, and hybrid approaches applied to real-world datasets. Through theoretical explanations, case studies, and practical examples, readers learn how these techniques can improve learning efficiency, feature selection, and hyperparameter tuning. The book also discusses challenges, best practices, and performance evaluation methods in evolutionary machine learning. By the end, readers gain the knowledge and tools to apply metaheuristic and evolutionary strategies to enhance machine learning solutions in research and industry.
نقد و بررسیها
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