- author = "Saemundur O. Haraldsson and John R. Woodward and Alexander E. I. Brownlee and Albert V. Smith and Vilmundur Gudnason",
- title = "Genetic Improvement of Runtime in a Bioinformatics Application",
- booktitle = "GI-2017",
- year = "2017",
- editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer",
- address = "Berlin",
- month = "15-19 " # jul,
- keywords = "genetic algorithms, genetic programming, genetic improvement, software performance, Search-based software engineering, SBSE, Execution Time, Landscape, Bioinformatics",
-
URL = "
http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/haraldsson2017_bioinformatics.pdf",
-
DOI = "
10.1145/3067695.3082526",
- size = "8 pages",
- 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.",
-
notes = "missing values, SNPs. Learn from smallest dataset but
mutated C code applicable to larger dataset. Macro
mutation: moving lines of code (eg delete line 321) and
micromutation, in statement token changes, eg add one
to integer constant, Replace col_new++ with ++col_new.
GI run 8 hours. No semantic change. 'Software is Not
Fragile' Significant but tiny speedup.
The Icelandic Heart Association",
