|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
Smith, Albert V
|Title:||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, New York: Association for Computing Machinery, Inc. GECCO 2017: The Genetic and Evolutionary Computation Conference, 15.7.2017 - 19.7.2017, Berlin, Germany, pp. 1521-1528.|
|Conference Name:||GECCO 2017: The Genetic and Evolutionary Computation Conference|
|Conference Location:||Berlin, Germany|
|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.|
|Status:||Book Chapter: publisher version|
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