Research output

Conference Paper (in Formal Publication) ()

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.

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.

StatusPublished
AuthorsHaraldsson Seamundur, Woodward John, Brownlee Alexander, Smith Albert V, Gudnason Vilmundur
Publication date2017
Date of public distribution07/2017
Date accepted by journal07/02/2017
PublisherAssociation for Computing Machinery, Inc
Place of publicationNew York
ISBN 9781450349390 (ISBN)
LanguageEnglish
© University of Stirling FK9 4LA Scotland UK • Telephone +44 1786 473171 • Scottish Charity No SC011159
My Portal