GLHF: General Learned Evolutionary Algorithm Via Hyper Functions

• URL: http://arxiv.org/abs/2405.03728v1
• Date: Mon, 6 May 2024 09:11:49 GMT
• Title: GLHF: General Learned Evolutionary Algorithm Via Hyper Functions
• Authors: Xiaobin Li, Kai Wu, Yujian Betterest Li, Xiaoyu Zhang, Handing Wang, Jing Liu,
• Abstract summary: General pre-trained optimization model (GPOM) outperforms state-of-the-art evolutionary algorithms and pretrained optimization models (POMs) GPOM exhibits robust generalization capabilities across diverse task distributions, dimensions, population sizes, and optimization horizons.
• Score: 16.391389860521134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained Optimization Models (POMs) leverage knowledge gained from optimizing various tasks, providing efficient solutions for new optimization challenges through direct usage or fine-tuning. Despite the inefficiencies and limited generalization abilities observed in current POMs, our proposed model, the general pre-trained optimization model (GPOM), addresses these shortcomings. GPOM constructs a population-based pretrained Black-Box Optimization (BBO) model tailored for continuous optimization. Evaluation on the BBOB benchmark and two robot control tasks demonstrates that GPOM outperforms other pretrained BBO models significantly, especially for high-dimensional tasks. Its direct optimization performance exceeds that of state-of-the-art evolutionary algorithms and POMs. Furthermore, GPOM exhibits robust generalization capabilities across diverse task distributions, dimensions, population sizes, and optimization horizons.

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