Differential Evolution
Differential Evolution (DE) is a population-based stochastic optimization algorithm used to find global minima or maxima of multidimensional real-valued functions. It operates by iteratively improving candidate solutions through mutation, crossover, and selection processes, inspired by evolutionary principles. DE is particularly effective for continuous optimization problems where gradient information is unavailable or unreliable.
Developers should learn Differential Evolution when tackling complex optimization problems in fields like engineering design, machine learning hyperparameter tuning, or financial modeling, where traditional gradient-based methods fail due to non-differentiability, noise, or high dimensionality. It's valuable for its simplicity, robustness, and ability to handle non-linear, non-convex, and multi-modal functions without requiring derivative calculations.