@inproceedings{71044cfa7da44ea5b497d759a68fa275,
title = "Genetic Improvement of Runtime and its Fitness Landscape in a Bioinformatics Application",
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.",
keywords = "Bioinformatics, Execution Time, Genetic Improvement, Landscape",
author = "Haraldsson, {Saemundur O.} and Woodward, {John R.} and Brownlee, {Alexander E.I.} and Smith, {Albert V.} and Vilmundur Gudnason",
year = "2017",
month = jul,
day = "15",
doi = "10.1145/3067695.3082526",
language = "English",
series = "GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion",
publisher = "Association for Computing Machinery, Inc",
pages = "1521--1528",
booktitle = "GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion",
note = "2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 ; Conference date: 15-07-2017 Through 19-07-2017",
}