Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916660445708288 |
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| author | Guo, Hongshu Ma, Sijie Huang, Zechuan Hu, Yuzhi Ma, Zeyuan Zhang, Xinglin Gong, Yue-Jiao |
| author_facet | Guo, Hongshu Ma, Sijie Huang, Zechuan Hu, Yuzhi Ma, Zeyuan Zhang, Xinglin Gong, Yue-Jiao |
| contents | Recently, Meta-Black-Box-Optimization (MetaBBO) methods significantly enhance the performance of traditional black-box optimizers through meta-learning flexible and generalizable meta-level policies that excel in dynamic algorithm configuration (DAC) tasks within the low-level optimization, reducing the expertise required to adapt optimizers for novel optimization tasks. Though promising, existing MetaBBO methods heavily rely on human-crafted feature extraction approach to secure learning effectiveness. To address this issue, this paper introduces a novel MetaBBO method that supports automated feature learning during the meta-learning process, termed as RLDE-AFL, which integrates a learnable feature extraction module into a reinforcement learning-based DE method to learn both the feature encoding and meta-level policy. Specifically, we design an attention-based neural network with mantissa-exponent based embedding to transform the solution populations and corresponding objective values during the low-level optimization into expressive landscape features. We further incorporate a comprehensive algorithm configuration space including diverse DE operators into a reinforcement learning-aided DAC paradigm to unleash the behavior diversity and performance of the proposed RLDE-AFL. Extensive benchmark results show that co-training the proposed feature learning module and DAC policy contributes to the superior optimization performance of RLDE-AFL to several advanced DE methods and recent MetaBBO baselines over both synthetic and realistic BBO scenarios. The source codes of RLDE-AFL are available at https://github.com/GMC-DRL/RLDE-AFL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18061 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning Guo, Hongshu Ma, Sijie Huang, Zechuan Hu, Yuzhi Ma, Zeyuan Zhang, Xinglin Gong, Yue-Jiao Neural and Evolutionary Computing Machine Learning Recently, Meta-Black-Box-Optimization (MetaBBO) methods significantly enhance the performance of traditional black-box optimizers through meta-learning flexible and generalizable meta-level policies that excel in dynamic algorithm configuration (DAC) tasks within the low-level optimization, reducing the expertise required to adapt optimizers for novel optimization tasks. Though promising, existing MetaBBO methods heavily rely on human-crafted feature extraction approach to secure learning effectiveness. To address this issue, this paper introduces a novel MetaBBO method that supports automated feature learning during the meta-learning process, termed as RLDE-AFL, which integrates a learnable feature extraction module into a reinforcement learning-based DE method to learn both the feature encoding and meta-level policy. Specifically, we design an attention-based neural network with mantissa-exponent based embedding to transform the solution populations and corresponding objective values during the low-level optimization into expressive landscape features. We further incorporate a comprehensive algorithm configuration space including diverse DE operators into a reinforcement learning-aided DAC paradigm to unleash the behavior diversity and performance of the proposed RLDE-AFL. Extensive benchmark results show that co-training the proposed feature learning module and DAC policy contributes to the superior optimization performance of RLDE-AFL to several advanced DE methods and recent MetaBBO baselines over both synthetic and realistic BBO scenarios. The source codes of RLDE-AFL are available at https://github.com/GMC-DRL/RLDE-AFL. |
| title | Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2503.18061 |