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Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
International Conference on Machine Learning
Bayesian optimization methods are promising for the optimization of black-box functions that are expensive to evaluate. In this paper, a novel batch Bayesian optimization approach is proposed. The parallelization is realized via a multi-objective ensemble of multiple acquisition functions. In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions. The batch of inputs are then selected fromdblp:conf/icml/Lyu0YZ018 fatcat:papsedgtubgtjp67ynitj6mxam