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

Volume 11, Issue 2 (April 2020), pp. 29–35

Download PDF

@article{jocse-11-2-5,
  author={Nicholas Kellas and Michael N. Groves},
  title={Improvement of the Evolutionary Algorithm on the Atomic Simulation Environment Though Intuitive Starting Population Creation and Clustering},
  journal={The Journal of Computational Science Education},
  year=2020,
  month=apr,
  volume=11,
  issue=2,
  pages={29--35},
  doi={https://doi.org/10.22369/issn.2153-4136/11/2/5}
}

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.