Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis

arXiv:2403.14413v1 Announce Type: cross
Abstract: Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This often leads to significant computational resources being consumed for simulations. Bayesian Optimization (BO) and Surrogate-Assisted Evolutionary Algorithm (SAEA) are two widely used gradient-free optimization techniques employed to address such challenges. Both approaches follow a similar iterative procedure that relies on surrogate models to guide the search process. This paper aims to elucidate the similarities …

abstract algorithm analysis applications arxiv bayesian box comparative analysis computational cs.lg cs.ne free gradient input-output interactions leads optimization resources simulations through type uncertainty world