Dynamic instance sampling for multi-objective automatic algorithm configuration
Multi-objective automatic algorithm configuration alleviates the tedious parameter tuning for users by optimizing both the performance and efficiency of the target algorithm. Its evaluation requires performing multiple runs for each configuration on an instance set, making the computational cost exp...
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| Published in: | Swarm and evolutionary computation Vol. 97; p. 102008 |
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| Format: | Journal Article |
| Language: | English |
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01.08.2025
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| ISSN: | 2210-6502 |
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| Abstract | Multi-objective automatic algorithm configuration alleviates the tedious parameter tuning for users by optimizing both the performance and efficiency of the target algorithm. Its evaluation requires performing multiple runs for each configuration on an instance set, making the computational cost expensive. Especially for real-world application problems, it is crucial to reduce computational costs under limited budgets. However, when the instance set is large, model-based approaches struggle to further reduce the high cost of configuration evaluations, which remains a significant challenge. To address this, we propose a Kriging-assisted Two_Arch2 with dynamic instance sampling algorithm, which aims to reduce the high costs of configuration evaluations by lowering the fidelity of the evaluations. Specifically, we align the number of evaluation instances with the evaluation fidelity and design a dynamic instance sampling strategy to effectively control the frequency of new instance sampling, enabling fidelity switching. Moreover, a trade-off configuration selection method is proposed to assist users in choosing configurations when preferences are unclear. The proposed method has been tested on numerous instances from the BBOB benchmark suite. The experimental results demonstrate that the performance of the proposed algorithm outperforms other state-of-the-art methods. |
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| AbstractList | Multi-objective automatic algorithm configuration alleviates the tedious parameter tuning for users by optimizing both the performance and efficiency of the target algorithm. Its evaluation requires performing multiple runs for each configuration on an instance set, making the computational cost expensive. Especially for real-world application problems, it is crucial to reduce computational costs under limited budgets. However, when the instance set is large, model-based approaches struggle to further reduce the high cost of configuration evaluations, which remains a significant challenge. To address this, we propose a Kriging-assisted Two_Arch2 with dynamic instance sampling algorithm, which aims to reduce the high costs of configuration evaluations by lowering the fidelity of the evaluations. Specifically, we align the number of evaluation instances with the evaluation fidelity and design a dynamic instance sampling strategy to effectively control the frequency of new instance sampling, enabling fidelity switching. Moreover, a trade-off configuration selection method is proposed to assist users in choosing configurations when preferences are unclear. The proposed method has been tested on numerous instances from the BBOB benchmark suite. The experimental results demonstrate that the performance of the proposed algorithm outperforms other state-of-the-art methods. |
| ArticleNumber | 102008 |
| Author | Wang, Handing Li, Yuchen |
| Author_xml | – sequence: 1 givenname: Yuchen orcidid: 0009-0002-3169-8977 surname: Li fullname: Li, Yuchen email: ycli_7@stu.xidian.edu.cn – sequence: 2 givenname: Handing orcidid: 0000-0002-4805-3780 surname: Wang fullname: Wang, Handing email: hdwang@xidian.edu.cn |
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| Cites_doi | 10.1145/3610536 10.1109/TEVC.2014.2350987 10.1109/TCYB.2015.2456187 10.1162/106365601750190398 10.1162/106365603321828970 10.1080/00401706.2000.10485979 10.11144/Javeriana.upsy10-2.cdcp 10.1162/evco_a_00371 10.1109/TEVC.2014.2304415 10.1137/S1052623496307510 10.1109/TEVC.2021.3073648 10.1007/BF00932614 10.1111/j.2517-6161.1995.tb02031.x 10.1023/A:1015059928466 10.1038/scientificamerican0792-66 10.1109/TEVC.2014.2339823 10.3389/fnins.2020.00667 10.1109/TEVC.2021.3102863 10.1137/16M1082469 10.1109/TEVC.2006.872133 10.1109/TEVC.2005.851274 10.1109/TEVC.2022.3226837 10.1109/4235.996017 10.1109/CEC45853.2021.9504792 |
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| Keywords | Automatic algorithm configuration Multi-objective optimization Dynamic instance sampling Parameter tuning |
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