Inferior solutions in Gaussian EDA: Useless or useful?

Abstract

Estimation of distribution algorithms (EDAs) are a special class of model-based evolutionary algorithms (EAs). To improve the performance of traditional EDAs, many remedies were suggested, which mainly focused on estimating a suitable probability distribution model with superior solutions. Different from existing research ideas, this paper tries to enhance EDA by exploiting the potential value of inferior solutions, where Gaussian EDA is taken as an example. It will be shown that, after a simple repair operation, inferior solutions could be surprisingly useful in adjusting the covariance matrix of Gaussian model, then a better search direction and a more proper search scale can be obtained. Since the aim of Inferior Solution Repairing (ISR) operator is not to directly improve the quality of inferior solutions, but to make them closer to superior ones, it can be implemented in a simple way. Combining ISR and traditional Gaussian EDA, a new EDA variant named ISR-EDA is developed. Comparison with existing EDAs and some other state-of-the-art EAs on benchmark functions demonstrates that ISR-EDA is efficient and competitive.