Mutation Strength

Mutation strength adaptation in evolutionary algorithms focuses on dynamically adjusting the magnitude of mutations during the search for optimal solutions, aiming to balance exploration and exploitation. Current research investigates various adaptation strategies, including cumulative step-size adaptation and self-adaptation within algorithms like Evolution Strategies (ES) and Covariance Matrix Adaptation Evolution Strategies (CMA-ES), often exploring their behavior across different population sizes and problem dimensions. These studies are crucial for improving the efficiency and robustness of evolutionary algorithms in tackling complex optimization problems, particularly in high-dimensional spaces. Efficient mutation strength adaptation leads to faster convergence and better solutions in various applications, from engineering design to machine learning.