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arXiv · 2611.04620

Refined CMA-ES for CEC 2026 Bound-Constrained Single-Objective Optimization

Adam Stelmaszczyk·Rafał Biedrzycki·Jarosław Arabas
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Abstract

We present a simplified and parameter-adjusted variant of CMA-ES designed for the CEC 2026 Bound-Constrained Single-Objective Optimization competition. Unlike typical algorithmic developments that increase complexity by adding mechanisms, Refined CMA-ES systematically removes components that do not improve performance, such as the flat-land escape, while retaining standard features that provide measurable benefits. Adjusting key parameters (population size and initial step-size) yields substantial gains, outperforming the baseline CMA-ES and several state-of-the-art competitors. The paper highlights the practical relevance of individual CMA-ES components under the CEC 2026 ranking, which equally weights solution accuracy and convergence speed, demonstrating that simplification combined with improved parameter settings can lead to superior performance.

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Adam Stelmaszczyk, Rafał Biedrzycki, Jarosław Arabas. 2026-11-03. Refined CMA-ES for CEC 2026 Bound-Constrained Single-Objective Optimization. https://doi.org/10.1109/cec69861.2026.11727142

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