Quantitative Performance Analysis of Stopping Criteria for CMA-ES
This paper analyzes the effectiveness of 11 different stopping criteria within the CMA-ES black-box optimization algorithm. Researchers quantitatively evaluated these criteria on the BBOB function set, focusing on their ability to accurately determine when to halt the search process without wasting computational resources. The study found that `tolflatfitness` and `tolfun` were frequently the first criteria to be triggered, while `tolfunhist` and the combined portfolio of criteria achieved the highest stopping accuracy. AI
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