Improvement of the Evolutionary Algorithm on the Atomic Simulation Environment Though Intuitive Starting Population Creation and Clustering

The Evolutionary algorithm (EA), on the Atomic Simulation Environment (ASE), provides a means to find the lowest energy conformation molecule of a given stoichiometry. In this study we examine the ways in which the initial population of molecules affect the success of the EA. We have added a set of rules to the way in which the molecules are created that leads to more chemically relevant structures using chemical intuition. We have also implemented a clustering program that selects molecules that differ from each other from a large pool of molecules to form the initial population. Through testing of EA runs with and without clustering and intuitive population creation, the following success rates were obtained; no intuition and no clustering, 28±3%, no intuition with clustering, 31±4%, with fixed intuition but without clustering, 49±5%, with fixed intuition and clustering, 49±4%, with variable intuition and without clustering, 47±4%, and with variable intuition and clustering, 50±3%. A significant increase in success rate was found when implementing intuitive population creation while clustering the initial population seems to marginally help as the population becomes more diverse.