ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
This addresses the problem of limited generalizability in configuring evolutionary algorithms for researchers and practitioners, representing a significant step toward an automatic, all-purpose agent, though it builds incrementally on prior meta-learning approaches.
The paper tackles the limitation of existing meta-learning methods for black-box optimization that are tailored to specific evolutionary algorithms (EAs) by introducing ConfigX, a modular framework that learns a universal configuration agent via multitask reinforcement learning, achieving robust zero-shot generalization and outperforming state-of-the-art baselines.
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.