- author = "Oliver Krauss and Hanspeter Moessenboeck and Michael Affenzeller",
- title = "Towards Knowledge Guided Genetic Improvement",
- booktitle = "GI @ ICSE 2020",
- year = "2020",
- month = "3 " # jul,
- editor = "Shin Yoo and Justyna Petke and Westley Weimer and Bobby R. Bruce",
- publisher = "ACM",
- address = "internet",
- pages = "293--294",
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Grammar-guided Genetic Programming, search space, genetic improvements, evolutionary operators, performance approximation, operator graph, AST",
- isbn13 = "978-1-4503-7963-2",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gi2020/krauss_towards_2020.pdf",
-
video_url = "
https://youtu.be/GsNKCifm44A",
-
DOI = "
doi:10.1145/3387940.3392172",
- size = "2 pages",
- abstract = "We outline a combination of Grammar Guided Genetic Programming with Tree Genetic Programming. Instead of using a grammar directly, an operator graph based on that grammar is created, that is responsible for producing abstract syntax trees. Each operator contains knowledge about the grammar symbol it represents and returns only trees valid according to user-defined restrictions such as depth, complexity and approximated run-time performance. Initial Results show that the approach reduces invalid individuals in an evolutionary run, while supporting the extension towards further non-functional features",
- eventtitle = "2020 {{IEEE}}/{{ACM International Workshop}} on {{Genetic Improvement}} ({{GI}})",
-
notes = "STGP? Cites
\cite{reference/ai/ManriqueRR09}
...turning the grammar itself into an operator graph...
model run-time of each operator. Sum according to if/else guessed probablities and estimated number of for loop iterations.
Video: https://youtu.be/GsNKCifm44A (start 2:00:47, 2:09:52 end 2:12:14)
Slides: http://geneticimprovementofsoftware.com/slides/gi2020icse/towards_knowledge_guided_slides.pdf
http://geneticimprovementofsoftware.com/gi2020icse.html",
