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The Optimizer Reached One Good Point Before Spreading Out
Across 446 pairwise comparisons, a two-stage Bayesian-optimization strategy won 72.9%, tied 21.1% and lost 6.1%.
Summary
Across 446 pairwise comparisons, a two-stage Bayesian-optimization strategy won 72.9%, tied 21.1% and lost 6.1%.
Multi-objective Bayesian optimization normally tries to approach the Pareto front and preserve diversity at the same time. Under tight evaluation budgets, the authors argue that this split attention can prevent either goal from succeeding. Their converge-then-diversify strategy first drives the search toward a single Pareto point, then spreads solutions across the front. Two implementations based on standard acquisition functions statistically outperformed state-of-the-art methods in 72.9% of 446 pairwise comparisons, tied in 21.1% and lost in 6.1%, with the largest gains in tight-budget and high-dimensional settings.
Why it matters
Across 446 pairwise comparisons, a two-stage Bayesian-optimization strategy won 72.9%, tied 21.1% and lost 6.1%.
Limits and context
No additional limitation was separately recorded.
Key claims
Across 446 pairwise comparisons, a two-stage Bayesian-optimization strategy won 72.9%, tied 21.1% and lost 6.1%.
Evidence: source-2026-09-15-004
Sources
- arXiv preprint 2609.13396arXiv · primary research
Corrections
No corrections have been recorded for this story.