Conference Proceeding

Visualising the Search Landscape of the Triangle Program

Citation

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 (eds.) EuroGP 2017: Genetic Programming. Lecture Notes in Computer Science, 10196. The 20th European Conference on Genetic Programming (EuroGP), Amsterdam, The Netherlands, 19.04.2017-21.04.2017. Cham, Switzerland: Springer, pp. 96-113. https://doi.org/10.1007/978-3-319-55696-3_7

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.

Keywords
genetic improvement; genetic algorithms; genetic programming; software engineering; heuristic methods; test equivalent higher order mutants; fitness landscape; local search

StatusPublished
Title of seriesLecture Notes in Computer Science
Number in series10196
Publication date31/12/2017
Publication date online15/03/2017
URLhttp://hdl.handle.net/1893/24848
PublisherSpringer
Place of publicationCham, Switzerland
ISSN of series0302-9743
ISBN978-3-319-55695-6
eISBN978-3-319-55696-3
ConferenceThe 20th European Conference on Genetic Programming (EuroGP)
Conference locationAmsterdam, The Netherlands
Dates

Research centres/groups