Adaptive Exploratory Landscape Analysis for LLM-based Meta-Black-Box Optimization

Large Language Models (LLMs) have recently advanced Meta-Black-Box Optimization (MetaBBO) by enabling optimizer design with reduced expert intervention, while existing approaches typically rely on fixed optimization-state representations, such as predefined Exploratory Landscape Analysis (ELA) indicators. We argue that adaptive optimization-state perception is more effective than fixed optimization-state representations for improving MetaBBO performance. Motivated by this idea, we propose AdaELA, a plug-and-play adaptive ELA selection framework for LLM-based MetaBBO. AdaELA dynamically selects informative ELA indicators through a population-aware self-attention encoder and a cross-attention-based selection mechanism, and provides the selected landscape information to LLM-based meta-policies for optimizer control. We formulate the training of AdaELA as a multi-task optimization problem and optimize its parameters using a neuroevolution strategy. Extensive experiments demonstrate that AdaELA consistently improves different LLM-based MetaBBO methods, generalizes to unseen optimization tasks, and enables further performance improvements through fine-tuning. This work highlights adaptive optimization-state perception as a promising direction toward more generalizable and effective MetaBBO.