Authors: Jiacheng Chen, Zeyuan Ma, Hongshu Guo, Yining Ma, Jie Zhang, Yue-jiao Gong

Published on: February 04, 2024

Impact Score: 8.3

Arxiv code: Arxiv:2402.02355

Summary

• What is new: Introduction of a novel framework, \\textsc{Symbol}, for the automated discovery of black-box optimizers through symbolic equation learning, deviating from traditional methods that rely on hand-crafted optimizers.
• Why this is important: Existing Meta-learning for Black-Box Optimization (MetaBBO) methods are limited by the constraints of predefined, hand-crafted optimizers.
• What the research proposes: The proposed \\textsc{Symbol} framework utilizes a Symbolic Equation Generator (SEG) to dynamically create optimization rules tailored for specific tasks, bolstered by reinforcement learning strategies for efficient meta-learning.
• Results: Optimizers generated by \\textsc{Symbol} outperform state-of-the-art BBO and MetaBBO baselines and demonstrate remarkable zero-shot generalization capabilities across varied tasks.

Technical Details

Technological frameworks used: \\textsc{Symbol}

Models used: Symbolic Equation Generator (SEG), reinforcement learning strategies

Data used: Not specified

Potential Impact

Markets focusing on optimization software, AI development platforms, and companies in industries reliant on optimization problems such as logistics, manufacturing, and AI-driven decision-making could be significantly impacted.

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