EvoX MetaBench: Composable Benchmarking for Meta-Black-Box Optimization at Scale

Meta-black-box optimization (MetaBBO) automates optimizer design through learning or design search. However, method-specific implementations often couple components, which complicates controlled comparisons of their effects and interactions. Such comparisons also incur substantial computational costs from repeated optimization runs across problems and random seeds. To address these challenges, we introduce EvoX MetaBench, a composable framework and benchmark for MetaBBO at scale. Its task-specific interfaces support component assembly for algorithm selection, algorithm configuration, solution manipulation, and algorithm generation. Researchers can build custom workflows or instantiate baseline recipes assembled from the framework's components, then substitute and recombine compatible components under shared experimental conditions. The benchmark provides over 11,000 problem instances and instance-generation recipes across numerical, combinatorial, and real-world optimization, with explicit training, validation, and test manifests for reproducible comparisons. To support these workflows at scale, the framework builds on EvoX to batch optimization runs on GPUs. Experiments show that GPU execution accelerates LDE training and testing by up to $107.6\times$ and $174.9\times$, respectively, compared with CPU execution. Component studies show that configuration gains depend on population size, useful state information depends on the optimizer, and the link between parent and offspring quality depends on the generation operator.