Pessimistic asynchronous sampling in high-cost Bayesian optimization

Abstract

Asynchronous Bayesian optimization is a recently implemented technique that allows for parallel operation of experimental systems and disjointed workflows in autonomous experimentation settings. Contrasting with serial Bayesian optimization, which individually selects experiments one at a time after conducting a measurement for each experiment, asynchronous policies sequentially assign multiple experiments before measurements can be taken and evaluates new measurements continually as they are made available. This technique allows for faster data generation and therefore faster optimization of an experimental space. This work extends the capabilities of asynchronous optimization methods beyond prior studies by evaluating policies that incorporate pessimistic and random predictions in the training data set. The conventional realistic prediction method and five additional asynchronous policies were evaluated in a simulated environment and benchmarked with serial sampling. In many of the tested scenarios, the pessimistic prediction asynchronous policy reached optimum experimental conditions in significantly fewer experiments than both existing asynchronous methods and serial policies, and proved to be less susceptible to convergence onto local optima at higher dimensions. Without accounting for the faster sampling rate enabled by asynchronous operation, the pessimistic asynchronous algorithm could result in more efficient algorithm driven optimization of high-cost experimental spaces. Accounting for sampling rate, the presented asynchronous algorithm could facilitate faster and more robust optimization in parallel autonomous experimentation settings.