Papers › {PF}²ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian...
{PF}²ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization
Jixiang Qing, Henry B. Moss, Tom Dhaene, Ivo Couckuyt
We present Parallel Feasible Pareto Frontier Entropy Search ({PF}²ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization supporting unknown constraints and batch query. Due to the complexity of characterizing the mutual information between candidate evaluations and (feasible) Pareto frontiers, existing approaches must either employ crude approximations that significantly hamper their performance or rely on expensive inference schemes that substantially increase the optimization's computational overhead. By instead using a variational lower bound, {PF}²ES provides a low-cost and accurate estimate of the mutual information. We benchmark {PF}²ES against other information-theoretic acquisition functions, demonstrating its competitive performance for optimization across synthetic and real-world design problems.
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