Recent advances in Meta-learning for Black-Box Optimization (MetaBBO) have shown the potential of using neural networks to dynamically configure evolutionary algorithms (EAs), enhancing their performance and adaptability across various BBO instances. However, they are often tailored to a specific EA, which limits their generalizability and necessitates retraining or redesigns for different EAs and optimization problems. To address this limitation, we introduce ConfigX, a new paradigm of the MetaBBO framework that is capable of learning a universal configuration agent (model) for boosting diverse EAs. To achieve so, our ConfigX first leverages a novel modularization system that enables the flexible combination of various optimization sub-modules to generate diverse EAs during training. Additionally, we propose a Transformer-based neural network to meta-learn a universal configuration policy through multitask reinforcement learning across a designed joint optimization task space. Extensive experiments verify that, our ConfigX, after large-scale pre-training, achieves robust zero-shot generalization to unseen tasks and outperforms state-of-the-art baselines. Moreover, ConfigX exhibits strong lifelong learning capabilities, allowing efficient adaptation to new tasks through fine-tuning. Our proposed ConfigX represents a significant step toward an automatic, all-purpose configuration agent for EAs.

翻译：近年来，元学习在黑箱优化（MetaBBO）领域的最新进展表明，利用神经网络动态配置进化算法（EAs）具有巨大潜力，能够提升算法在各种黑箱优化实例中的性能与适应性。然而，现有方法通常针对特定进化算法定制，这限制了其泛化能力，且在面对不同进化算法和优化问题时需要重新训练或重新设计。为克服这一局限，我们提出ConfigX——一种新型MetaBBO框架范式，能够学习通用的配置智能体（模型）以增强多样化进化算法的性能。为实现这一目标，ConfigX首先采用创新的模块化系统，在训练过程中通过灵活组合各类优化子模块来生成多样化的进化算法。此外，我们提出基于Transformer的神经网络架构，通过在多任务强化学习框架下对设计的联合优化任务空间进行元学习，从而获得通用配置策略。大量实验验证表明，经过大规模预训练的ConfigX能够对未见任务实现鲁棒的零样本泛化，其性能优于当前最先进的基线方法。此外，ConfigX展现出强大的持续学习能力，可通过微调高效适应新任务。我们提出的ConfigX标志着向构建自动化、通用型进化算法配置智能体迈出了重要一步。