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

Saemundur O. Haraldsson, John R. Woodward, Alexander E.I. Brownlee, Albert V. Smith, Vilmundur Gudnason

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

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 fiat. We also confirm that human written code is robust with respect to small edits to the source code.

Original languageEnglish
Title of host publicationGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1521-1528
Number of pages8
ISBN (Electronic)9781450349390
DOIs
StatePublished - 15 Jul 2017
Externally publishedYes
Event2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 - Berlin, Germany
Duration: 15 Jul 201719 Jul 2017

Publication series

NameGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion

Conference

Conference2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017
Country/TerritoryGermany
CityBerlin
Period15/07/1719/07/17

Keywords

  • Bioinformatics
  • Execution Time
  • Genetic Improvement
  • Landscape

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