Enhancing Evolution Strategies with Evolution Path Bias
Oliver Krämer · 2023
Evolution Strategies (ES) have emerged as a powerful and effective method for optimization and reinforcement learning tasks, largely due to their simplicity and scalability.However, current ES techniques can be limited in their capacity to quickly converge on the optimal solution.In this paper, we propose a novel approach to enhance ES by incorporating an evolution path-informed bias in the Gaussian mutation operator.This bias is designed to facilitate faster descent on decreasing functions.Our method leverages the evolution path, which represents the historical search directions, to intelligently bias the Gaussian mutation.By doing so, it enables the algorithm to be more sensitive to the underlying function's structure and adaptively exploit this information for more efficient exploration.We validate our approach through experiments on three benchmark functions: a linear function, we call Downhill function here, a Parabolic ridge, and a Sphere function.The results demonstrate that our evolution path-informed bias significantly accelerates convergence on in most of the cases.