Differential Evolution (DE) is a population-based optimization algorithm used in various fields of artificial intelligence and machine learning. It is particularly effective for optimizing complex, multi-dimensional functions and is known for its simplicity and efficiency. DE works by iteratively improving a population of candidate solutions based on the differences between randomly selected individuals. Common use cases include parameter tuning in machine learning models, feature selection, and solving optimization problems in engineering and finance.
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AI Fundamentals