RCMAES: A Robust CMA-ES Variant for CEC2026 Competition
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917448541798400 |
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| author | Muzakka, Khoirul Faiq Möller, Sören Finsterbusch, Martin |
| author_facet | Muzakka, Khoirul Faiq Möller, Sören Finsterbusch, Martin |
| contents | This paper proposes RCMAES, a novel variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for CEC benchmark optimization. RCMAES integrates a dimension-dependent nonlinear population-size reduction strategy with an adaptive restart mechanism within a pure CMA-ES framework. RCMAES is evaluated on three benchmark suites (CEC2017, CEC2020, and CEC2022) and compared with state-of-the-art DE algorithms as well as its closely related counterpart, BIPOP-aCMAES. Experimental results show that RCMAES achieves competitive and robust performance across all benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27138 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RCMAES: A Robust CMA-ES Variant for CEC2026 Competition Muzakka, Khoirul Faiq Möller, Sören Finsterbusch, Martin Neural and Evolutionary Computing This paper proposes RCMAES, a novel variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for CEC benchmark optimization. RCMAES integrates a dimension-dependent nonlinear population-size reduction strategy with an adaptive restart mechanism within a pure CMA-ES framework. RCMAES is evaluated on three benchmark suites (CEC2017, CEC2020, and CEC2022) and compared with state-of-the-art DE algorithms as well as its closely related counterpart, BIPOP-aCMAES. Experimental results show that RCMAES achieves competitive and robust performance across all benchmarks. |
| title | RCMAES: A Robust CMA-ES Variant for CEC2026 Competition |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2604.27138 |