- author = "Justyna Petke and Brad Alexander and Earl T. Barr and Alexander E. I. Brownlee and Markus Wagner and David R. White",
- title = "A Survey of Genetic Improvement Search Spaces",
- booktitle = "7th edition of GI @ GECCO 2019",
- year = "2019",
- month = jul # " 13-17",
- editor = "Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- address = "Prague, Czech Republic",
- pages = "1715--1721",
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, genetic improvement, Search-based Software Engineering, Program Repair, Search Space, Fitness Landscape, GI, APR, SBSE, NFR",
- isbn13 = "978-1-4503-6748-6",
-
URL = "
https://cs.adelaide.edu.au/~markus/pub/2019gecco-giSurvey.pdf",
-
DOI = "
doi:10.1145/3319619.3326870",
- size = "7 pages",
- abstract = "Genetic Improvement (GI) uses automated search to improve existing software. Most GI work has focused on empirical studies that successfully apply GI to improve softwares running time, fix bugs, add new features, etc. There has been little research into why GI has been so successful. For example, genetic programming has been the most commonly applied search algorithm in GI. Is genetic programming the best choice for GI? Initial attempts to answer this question have explored GIs mutation search space. This paper summarises the work published on this question to date",
-
notes = "
Also known as \cite{Petke:2019:GECCOcomp}
Also known as \cite{3326870} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)",
