DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915570888212480 |
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| author | Guo, Hongshu Ma, Zeyuan Ma, Yining Zhang, Xinglin Chen, Wei-Neng Gong, Yue-Jiao |
| author_facet | Guo, Hongshu Ma, Zeyuan Ma, Yining Zhang, Xinglin Chen, Wei-Neng Gong, Yue-Jiao |
| contents | Designing effective black-box optimizers is hampered by limited problem-specific knowledge and manual control that spans months for almost every detail. In this paper, we present \textit{DesignX}, the first automated algorithm design framework that generates an effective optimizer specific to a given black-box optimization problem within seconds. Rooted in the first principles, we identify two key sub-tasks: 1) algorithm structure generation and 2) hyperparameter control. To enable systematic construction, a comprehensive modular algorithmic space is first built, embracing hundreds of algorithm components collected from decades of research. We then introduce a dual-agent reinforcement learning system that collaborates on structural and parametric design through a novel cooperative training objective, enabling large-scale meta-training across 10k diverse instances. Remarkably, through days of autonomous learning, the DesignX-generated optimizers continuously surpass human-crafted optimizers by orders of magnitude, either on synthetic testbed or on realistic optimization scenarios such as Protein-docking, AutoML and UAV path planning. Further in-depth analysis reveals DesignX's capability to discover non-trivial algorithm patterns beyond expert intuition, which, conversely, provides valuable design insights for the optimization community. We provide DesignX's Python project at~ https://github.com/MetaEvo/DesignX. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17866 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization Guo, Hongshu Ma, Zeyuan Ma, Yining Zhang, Xinglin Chen, Wei-Neng Gong, Yue-Jiao Machine Learning Neural and Evolutionary Computing Designing effective black-box optimizers is hampered by limited problem-specific knowledge and manual control that spans months for almost every detail. In this paper, we present \textit{DesignX}, the first automated algorithm design framework that generates an effective optimizer specific to a given black-box optimization problem within seconds. Rooted in the first principles, we identify two key sub-tasks: 1) algorithm structure generation and 2) hyperparameter control. To enable systematic construction, a comprehensive modular algorithmic space is first built, embracing hundreds of algorithm components collected from decades of research. We then introduce a dual-agent reinforcement learning system that collaborates on structural and parametric design through a novel cooperative training objective, enabling large-scale meta-training across 10k diverse instances. Remarkably, through days of autonomous learning, the DesignX-generated optimizers continuously surpass human-crafted optimizers by orders of magnitude, either on synthetic testbed or on realistic optimization scenarios such as Protein-docking, AutoML and UAV path planning. Further in-depth analysis reveals DesignX's capability to discover non-trivial algorithm patterns beyond expert intuition, which, conversely, provides valuable design insights for the optimization community. We provide DesignX's Python project at~ https://github.com/MetaEvo/DesignX. |
| title | DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2505.17866 |