This paper presents gems, a novel method to accelerate fitness improvement in Evolutionary Algorithms (EAs). The paper develops the models, describes an experimental implementation, comments on characteristics of problem-domains that indicate where gems may be used, and suggests an explanation of the observed behavior. Experimental results show that gems accelerate the rate of fitness increase, and that the larger the problem instance, the larger the benefit. Runtime analysis shows that the method's fitness boost far outweighs its performance costs.
