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CMA-ES stopping criteria performance analyzed

By PulseAugur Editorial · [1 sources] ·

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

IMPACT Provides a detailed analysis of optimization techniques relevant to AI model training and hyperparameter tuning.

RANK_REASON Academic paper analyzing an optimization algorithm's components. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CMA-ES stopping criteria performance analyzed

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Academic paper analyzing an optimization algorithm's components. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Ryoji Tanabe ·

    Quantitative Performance Analysis of Stopping Criteria for CMA-ES

    Covariance matrix adaptation evolution strategy (CMA-ES) is a state-of-the-art black-box optimization algorithm. In general, CMA-ES uses a portfolio of multiple stopping criteria to automatically determine when to stop the search. This mechanism aims to avoid unnecessary consumpt…

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