Abstract—A black-box optimization problem is considered, in which the function to be optimized can only be expressed in terms of a complicated stochastic algorithm that takes a long time to evaluate. The value returned is required to be sufficiently near to a target value, and uses data that has a significant noise component. Bayesian Optimization with an underlying Gaussian Process is used as an optimization solution, and its effectiveness is measured in terms of the number of function evaluations required to attain the target. To improve results, a simple modification of the Gaussian Process ‘Lower Confidence Bound’ (LCB) acquisition function is proposed. The expression used for the confidence bound is squared in order to better comply with the target requirement. With this modification, much improved results compared to random selection methods and to other commonly used acquisition functions are obtained.
Index Terms—Acquisition function, bayesian optimization, gaussian process, loss distribution, monte carlo.
Peter Mitic is with the Dept. of Computer Science, UCL, London, UK (e-mail: p.mitic@ucl.ac.uk).
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Cite: Peter Mitic, "Improved Gaussian Process Acquisition for Targeted Bayesian Optimization," International Journal of Modeling and Optimization vol. 11, no. 1, pp. 12-18, 2021.
