GLHF: General Learned Evolutionary Algorithm Via Hyper Functions
<|reference_start|>GLHF: General Learned Evolutionary Algorithm Via Hyper Functions: 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.<|reference_end|>
@article{li2024glhf:,
    title={GLHF: General Learned Evolutionary Algorithm Via Hyper Functions},
    author={Xiaobin Li, Kai Wu, Yujian Betterest Li, Xiaoyu Zhang, Handing Wang,
  Jing Liu},
    journal={arXiv preprint arXiv:2405.03728},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03728},
    primaryClass={cs.NE cs.AI}
}