Evolution Strategies

Lilian Weng · 6 years ago

Evolution Strategies (ES) are black-box optimization algorithms that optimize vectors of real numbers by iteratively sampling from a probability distribution, evaluating fitness, and updating distribution parameters based on the best-performing samples. Simple Gaussian ES models the distribution with mean and standard deviation, while Covariance Matrix Adaptation ES (CMA-ES) adds a covariance matrix to track pairwise dependencies between samples, enabling faster adaptation of the exploration space. CMA-ES improves upon vanilla ES by separately controlling step size through evolution paths and adapting the covariance matrix through rank-one and rank-lambda updates, allowing more efficient optimization when gradients cannot be computed.