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cloud.google.com·6h ago
AI Research
arXiv CS.AI·27d ago·5 min read
Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation
TL;DR
A new approach called converge-then-diversify (CTD) is proposed for multi-objective Bayesian optimization (MOBO), separating the tasks of convergence and diversity into two distinct stages. Experimental results show that CTD outperforms existing methods in 72.9% of cases, particularly under tight evaluation budgets.
✦ Why It Matters
Consider implementing the CTD approach in your next optimization project to improve solution quality under budget constraints.
Key Takeaways
- Use the CTD approach to optimize under tight resource constraints.
- Focus on convergence first, then diversify solutions.
- Evaluate performance improvements with CTD in high-dimensional problems.
- Experiment with different acquisition functions for CTD implementations.
How It Works
CTD operates in two stages: the first stage emphasizes rapid convergence to a specific point on the Pareto front, while the second stage focuses on diversifying the solutions across the front. This separation allows for more effective use of limited evaluation budgets, as it prioritizes reaching a quality solution before exploring other options.