- author = "Oliver Krauss and Hanspeter Moessenboeck and Michael Affenzeller",
- title = "Mining Patterns from Genetic Improvement Experiments",
- booktitle = "GI-2019, ICSE workshops proceedings",
- year = "2019",
- editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer",
- pages = "28--29",
- address = "Montreal",
- month = "28 " # may,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, genetic improvement, Abstract Syntax Tree, Pattern Mining, Frequent Subgraph Mining",
- isbn13 = "978-1-7281-2268-7",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gi2019/Krauss_2019_GI.pdf",
-
DOI = "
doi:10.1109/GI.2019.00015",
- size = "2 pages",
- abstract = "When conducting genetic improvement experiments, a large amount of individuals (approx population size times generations) is created and evaluated. The corresponding experiments contain valuable data concerning the fitness of individuals for the defined criteria, such as run-time performance, memory use or robustness. This publication presents an approach to use this information in order to identify recurring context independent patterns in abstract syntax trees (ASTs). These patterns can be applied for restricting the search space (in the form of anti-patterns) or for grafting operators in the population. Future work includes an evaluation of this approach, as well as extending it with wildcards and class hierarchies for larger and more generalised patterns.",
-
notes = "SOTA, SLEUTH 2, Truffle 3, Graal
Slides: http://geneticimprovementofsoftware.com/slides/krauss2019mining_slides.pdf
GI-2019 http://geneticimprovementofsoftware.com
Part of \cite{Petke:2019:ICSEworkshop}",
