For materials applications, GAs have typically employed (semi-) empirical potentials (EP) to describe the potential energy surface (PES). The utilization of more accurate methods to describe the PES, such as a) Electronic mail: teve@dtu.dk b) Electronic mail: bligaard@stanford.edu density functional theory (DFT) has been limited, due to computational cost. To account for the increased computational cost of searching the PES directly with DFT, studies have often been limited in size, though these methods have successfully been used in a number of investigations. This study focuses on utilizing the GA to gain an understanding of chemical ordering within larger particles. Searching across a range of compositions is particularly important in the field of materials discovery, where composition can have a profound effect on the desired property e.g. catalytic activity. Further, the optimal composition may vary with the size of the nanoparticle. Therefore, the accurate description of chemical ordering is important; where, for certain motifs, the ordering is very complex. Focus is placed on expediting a fast unbiased homotop search by reducing the number of energy evaluations needed to explore the PES and locate the putative global minimum (GM) for a given template structure.