Research map / Probabilistic and scientific modeling / Bayesian optimization
Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Select input and fidelity vectors using output-space information gain per unit cost | MF-OSEMO · BC, SPP, ZDT3, DTLZ1, Rocket, NOC | convergence cost: 4.2 vs 2000; 190 vs 1950; 380 vs 2000; 100 vs 800; 250 vs 4000; 1200 vs 10000 | Helped |
| Use the MF-OSEMO-TG truncated-Gaussian approximation for lower-fidelity entropy (ablation) | MF-OSEMO · Rocket and NOC | convergence cost | Helped |
| Replace the MF-OSEMO-TG approximation with numerical integration (MF-OSEMO-NI) (ablation) | MF-OSEMO · Rocket and NOC | convergence cost | Hurt |
| Vary the number of Monte-Carlo samples from 1 to 10 or 100 (ablation) | MF-OSEMO-TG and MF-OSEMO-NI · BC and ZDT3 | convergence rate | No clear effect |
| Increase Monte-Carlo samples from 1 to 100 (ablation) | PESMO · BC and ZDT3 | convergence rate | Helped |
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