Research map / Probabilistic and scientific modeling / Bayesian optimization
Mixed-Variable Black-Box Optimisation Using Value Proposal Trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add tree-based regression and clustering to select high-potential candidate regions (ablation) | VPT · SVM-Boston and NASBench-101 | optimization performance | Helped |
| Add tree-based regression and clustering to select high-potential candidate regions (ablation) | VPT · Func2C, Func3C, and Reizman-Suzuki | optimization performance | Mixed |
| Select from the top clustered value-proposal region instead of considering every categorical combination (ablation) | VPT · XG-MNIST and NASBench-101 | mean wall-clock time per BO iteration: 5.2359 -> 4.7051; 4.2158 -> 4.1572 | Helped |
| Select from the top clustered value-proposal region instead of considering every categorical combination (ablation) | VPT · Func2C, Func3C, Reizman-Suzuki, and SVM-Boston | mean wall-clock time per BO iteration: 2.3063 -> 2.5666; 2.5372 -> 2.5346; 2.2138 -> 2.3854; 2.4609 -> 2.5397 | Mixed |
| Use global non-local candidate regions rather than local trust-region selection | VPT · mixed-variable benchmarks | optimization performance | Helped |
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