The goal of an Evolutionary Algorithm(EA) is to find the optimal solution to a given problem by evolving a set of initial potential solutions. When the problem is multi-modal, an EA will often become trapped in a suboptimal solution(premature convergence). The Scouting-Inspired Evolutionary Algorithm(SEA) is a relatively new technique that avoids premature convergence by determining whether a subspace has been explored sufficiently, and, if so, directing the search towards other parts of the system. Previous work has only focused on EAs with point mutation operators and standard selection techniques. This paper examines the effect of scouting on EA configurations that, among others, use crossovers and the Fitness-Uniform Selection Scheme(FUSS), a selection method that was specifically designed as means to avoid premature convergence. We will experiment with a variety of problems and show that scouting significantly improves the performance of all EA configurations presented.