Neural Exploratory Landscape Analysis
<|reference_start|>Neural Exploratory Landscape Analysis: Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions. Despite their success, a paradox remains: MetaBBO still rely on human-crafted Exploratory Landscape Analysis features to inform the meta-level agent about the low-level optimization progress. To address the gap, this paper proposes Neural Exploratory Landscape Analysis (NeurELA), a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely end-to-end fashion. NeurELA is pre-trained over a variety of MetaBBO algorithms using a multi-task neuroevolution strategy. Extensive experiments show that NeurELA achieves consistently superior performance when integrated into different and even unseen MetaBBO tasks and can be efficiently fine-tuned for further performance boost. This advancement marks a pivotal step in making MetaBBO algorithms more autonomous and broadly applicable.The source code of NeurELA can be accessed at https://anonymous.4open.science/r/Neur-ELA-303C.<|reference_end|>
@article{ma2024neural,
    title={Neural Exploratory Landscape Analysis},
    author={Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yue-Jiao Gong},
    journal={arXiv preprint arXiv:2408.10672},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10672},
    primaryClass={cs.LG cs.NE}
}