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.

翻译：近期元黑盒优化（MetaBBO）领域的研究表明，元训练神经网络能够有效指导黑盒优化器的设计，显著减少专家调参需求，并在复杂问题分布上提供鲁棒性能。尽管取得了这些成功，一个悖论依然存在：MetaBBO仍依赖人工设计的探索性景观分析特征来向元智能体传递底层优化进程信息。为弥补这一空白，本文提出神经探索性景观分析（NeurELA）——一种通过基于注意力的两阶段神经网络动态刻画景观特征的全端到端新型框架。NeurELA采用多任务神经进化策略，在多种MetaBBO算法上进行预训练。大量实验表明，NeurELA在集成至不同甚至未见过的MetaBBO任务时均能取得持续优越的性能，并可通过高效微调进一步提升表现。这一进展标志着MetaBBO算法向更高自主性与更广泛适用性迈出了关键一步。NeurELA的源代码可通过 https://anonymous.4open.science/r/Neur-ELA-303C 获取。