- author = "William B. Langdon and Nadarajen Veerapen and Gabriela Ochoa",
- title = "Visualising the Search Landscape of the {Triangle} Program",
- booktitle = "EuroGP 2017",
- year = "2017",
- editor = "Mauro Castelli and James McDermott and Lukas Sekanina",
- volume = "10196",
- series = "LNCS",
- pages = "96--113",
- address = "Amsterdam",
- month = "19-21 " # apr,
- organisation = "species",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, genetic improvement, software~engineering, SBSE, heuristic methods, test equivalent higher order mutants",
- isbn13 = "978-3-319-55695-6",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2017_EuroGP.pdf",
-
DOI = "
10.1007/978-3-319-55696-3_7",
-
code_url = "
https://github.com/wblangdon/triangle",
-
code_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/triangle.tar.gz",
-
data_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/egp2017/triangle/",
- size = "16 pages",
- abstract = "High order mutation analysis of a software engineering benchmark, including schema and local optima networks, suggests program improvements may not be as hard to find as is often assumed. 1) Bit-wise genetic building blocks are not deceptive and can lead to all global optima. 2) There are many neutral networks, plateaux and local optima, nevertheless in most cases near the human written C source code there are hill climbing routes including neutral moves to solutions.",
-
notes = "code
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/triangle.tar.gz
dataset
http://www.cs.ucl.ac.uk/staff/W.Langdon/egp2017/triangle/
See also \cite{langdon:2016:PPSNlandscape}. Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017",
