Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization
Researchers have introduced BBO-Pile, a novel open-source dataset containing over 500,000 optimization trajectories across nearly 3,100 black-boxes. This dataset aims to address the limitations of previous work, which relied on non-public or synthetic data, thereby hindering reproducibility and real-world generalization. By using BBO-Pile, foundation models for black-box optimization have been trained at various scales, demonstrating the effectiveness of large-scale pre-training for imitating optimization methods. AI
IMPACT Enables more reproducible and generalizable research in black-box optimization by providing a large-scale, open-source dataset.