Abstract
A novel method to tackle black-box optimization for time-varying problems is proposed. Using a Set Membership (SM) framework, the approach directly adjusts the uncertainty associated with old data points as new samples are introduced. Uninformative old samples are discarded, and the adjusted model guides the exploitation and exploration routines as characteristic of black-box optimization. With the proposed method, there is no need to estimate the time-related rate of change of the hidden function, as required in previous literature. We provide results of a benchmark test, comparing the performance of the proposed method to other approaches to time-varying black-box optimization, with promising results.
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