Stochastic Approximation Trackers for Model-Based Search

Abstract

In this paper, we propose multi-timescale, sequential algorithms for deterministic optimization which can find high-quality solutions. The algorithms fundamentally track the well-known derivative-free model-based search methods in an efficient and resourceful manner with additional heuristics to accelerate the scheme. Our approaches exhibit competitive performance on a selected few global optimization benchmark problems.