- author = "Aymeric Blot and Justyna Petke",
- title = "Using Genetic Improvement to Optimise Optimisation Algorithm Implementations",
- booktitle = "23{\`e}me congr{\`e}s annuel de la Soci{\'e}t{\'e} Fran{\c c}aise de Recherche Op{\'e}rationnelle et d'Aide {\`a} la D{\'e}cision, ROADEF'2022",
- year = "2022",
- editor = "Khaled Hadj-Hamou",
- address = "Villeurbanne - Lyon, France",
- month = "23--25 " # feb,
- organization = "INSA Lyon",
- keywords = "genetic algorithms, genetic programming, Genetic improvement, SBSE, Algorithm design, Software engineering, Optimisation, MOEA/D, NSGA-II, PyGGI , perf, IGD, XML, SrcML",
-
URL = "
https://hal.archives-ouvertes.fr/hal-03595447",
- pdf = "https://hal.archives-ouvertes.fr/hal-03595447/file/roadef_2022.pdf",
- hal_id = "hal-03595447",
- hal_version = "v1",
-
URL = "
http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_roadef_2022.pdf",
- size = "2 pages",
- abstract = "Genetic improvement (GI) \cite{Petke:gisurvey} uses automated search to improve existing software. It has been successfully used to fix software bugs or improve non-functional properties of software such as running time, memory usage, or energy consumption. Recently, it has been shown that genetic programming, the eponymous GI typical search algorithm, was outperformed by local search strategies \cite{blot:2021:tevc}. One result of that work was that GI was able to find interesting algorithmic changes in the implementation [Hui Li and Qingfu Zhang., 2009] of two state-of-the-art evolutionary algorithms, MOEA-D and NSGA-II. Here, we reproduce and extend this result with a simple local search, obtaining 10percent faster software variants with little to no impact on solution quality in 6/18 GI runs.",
-
notes = "Slides
http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_roadef_2022_slides.pdf
runtime limited to 3:45:00, five fold cross validation, 12percent reduction in execution time at validation CMOEAD::calc_distance(). Overfitting issues BUT 'generalised most of the time (12/18)'
https://roadef2022.sciencesconf.org/ https://roadef2022.sciencesconf.org/data/pages/LivretROADEF_2023.pdf",
