Surrogate models have shown to be effective in assisting evolutionary algorithms (EAs) for solving computationally expensive complex optimization problems. However, the effectiveness of existing surrogate-assisted still needs improved. A data-driven sampling (DESO) framework is proposed, where at each generation it randomly employs one two strategies, surrogate screening and local search based ...

Monte Carlo rendering algorithms generally rely on some form of importance sampling to evaluate the measurement equation. Most of these importance sampling methods only take local information into account, however, so the actual importance function used may not closely resemble the light distribution in the scene. In this paper, we present Table-driven Adaptive Importance Sampling (TAIS), a sam...