Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning

• URL: http://arxiv.org/abs/2503.18061v1
• Date: Sun, 23 Mar 2025 13:07:57 GMT
• Title: Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning
• Authors: Hongshu Guo, Sijie Ma, Zechuan Huang, Yuzhi Hu, Zeyuan Ma, Xinglin Zhang, Yue-Jiao Gong,
• Abstract summary: This paper introduces a novel MetaBBO method that supports automated feature learning during the meta-learning process.<n>We design an attention-based neural network with mantissa-exponent based embedding to transform the solution populations.<n>We also incorporate a comprehensive algorithm configuration space including diverse DE operators into a reinforcement learning-aided DAC paradigm.
• Score: 7.765689048808507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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