Optimization with noisy function evaluations

Abstract

In the optimization literature it is frequently assumed that the quality of solutions can be determined by calculating deterministic objective function values. Practical optimization problems, however, often require the evaluation of solutions through experimentation, stochastic simulation, sampling, or even interaction with the user. Thus, most practical problems involve noise. We empirically investigate the robustness of population-based versus point-based optimization methods on a range of parameter optimization problems when noise is added. Our results favor population-based optimization, and the evolution strategy in particular.