Conference Paper (in Formal Publication) ()
Langdon WB, Veerapen N & Ochoa G (2017) Visualising the Search Landscape of the Triangle Program In: McDermott J, Castelli M, Sekanina L, Haasdijk E, García-Sánchez P (ed.) EuroGP 2017: Genetic Programming, Cham, Switzerland: Springer. The 20th European Conference on Genetic Programming (EuroGP), 19.4.2017 - 21.4.2017, Amsterdam, The Netherlands, pp. 96-113.
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.
genetic improvement; genetic algorithms; genetic programming; software engineering; heuristic methods; test equivalent higher order mutants; fitness landscape; local search
|Editor||McDermott J, Castelli M, Sekanina L, Haasdijk E, García-Sánchez P|
|Authors||Langdon William B, Veerapen Nadarajen, Ochoa Gabriela|
|Title of series||Lecture Notes in Computer Science|
|Number in series||10196|
|Date of public distribution||15/03/2017|
|Date accepted by journal||09/01/2017|
|Place of publication||Cham, Switzerland|
|ISSN of series||0302-9743|