Genetic Improvement of Runtime and its Fitness Landscape in a Bioinformatics Application

Citation

Haraldsson S, Woodward J, Brownlee A, Smith AV & Gudnason V (2017) Genetic Improvement of Runtime and its Fitness Landscape in a Bioinformatics Application. In: 2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017. GECCO 2017: The Genetic and Evolutionary Computation Conference, Berlin, Germany, 15.07.2017-19.07.2017. New York: Association for Computing Machinery, Inc, pp. 1521-1528. https://doi.org/10.1145/3067695.3082526

Abstract
We present a Genetic Improvement (GI) experiment on ProbAbel, a piece of bioinformatics software for Genome Wide Association (GWA) studies. The GI framework used here has previously been successfully used on Python programs and can, with minimal adaptation, be used on source code written in other languages. We achieve improvements in execution time without the loss of accuracy in output while also exploring the vast fitness landscape that the GI framework has to search. The runtime improvements achieved on smaller data set scale up for larger data sets. Our findings are that for ProbAbel, the GI's execution time landscape is noisy but flat. We also confirm that human written code is robust with respect to small edits to the source code.