Curse and Blessing of Uncertainty in Evolutionary Algorithm Using Approximation

Abstract

Evolutionary frameworks that employ approximation models or surrogates for solving optimization problems with computationally expensive fitness functions may be referred as Surrogate-Assisted Evolutionary Algorithms (SAEA). In this paper, we present a study on the effects of uncertainty in the surrogate on SAEA. In particular, we focus on both the ‘ curse of uncertainty’ and ‘ blessing of uncertainty’ on evolutionary search, a notion borrowed from ‘ curse and blessing of dimensionality’ in [1]. Here, the ‘ curse of uncertainty’ refers to impairments due to the errors in the approximation. The ‘ blessing of uncertainty’ is less explicitly discussed in the literature, but refers to the benefits of approximation errors on evolutionary search. Empirical studies suggest that approximation errors lead to convergence at false global optima, but prove to be beneficial in some cases.