Genetic Improvement of Runtime in a Bioinformatics Application

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Haraldsson:2017a:GI,
  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 =          "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",
}

Genetic Programming entries for Saemundur Oskar Haraldsson John R Woodward Alexander E I Brownlee Albert V Smith Vilmundur Gudnason

Citations