Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913760506019840 |
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| author | Ma, Zeyuan Chen, Jiacheng Guo, Hongshu Gong, Yue-Jiao |
| author_facet | Ma, Zeyuan Chen, Jiacheng Guo, Hongshu Gong, Yue-Jiao |
| contents | Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis features to inform the meta-level agent about the low-level optimization progress. To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion. NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy. Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost. This advancement marks a pivotal step in making MetaBBO algorithms more autonomous and broadly applicable. The source code of NeurELA can be accessed at https://github.com/GMC-DRL/Neur-ELA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization Ma, Zeyuan Chen, Jiacheng Guo, Hongshu Gong, Yue-Jiao Machine Learning Neural and Evolutionary Computing Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis features to inform the meta-level agent about the low-level optimization progress. To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion. NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy. Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost. This advancement marks a pivotal step in making MetaBBO algorithms more autonomous and broadly applicable. The source code of NeurELA can be accessed at https://github.com/GMC-DRL/Neur-ELA. |
| title | Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2408.10672 |