Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25373
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Veerapen, Nadarajen
Daolio, Fabio
Ochoa, Gabriela
Contact Email: nve@cs.stir.ac.uk
Title: Modelling Genetic Improvement Landscapes with Local Optima Networks
Citation: Veerapen N, Daolio F & Ochoa G (2017) Modelling Genetic Improvement Landscapes with Local Optima Networks In: Proceedings of GECCO '17 Conference Companion, New York: ACM. Genetic Improvement Workshop 2017, 15.7.2017 - 15.7.2017, Berlin, Germany, pp. 1543-1548.
Issue Date: 2017
Conference Name: Genetic Improvement Workshop 2017
Conference Dates: 2017-07-15T00:00:00Z
Conference Location: Berlin, Germany
Abstract: Local optima networks are a compact representation of the global structure of a search space. They can be used for analysis and visualisation. This paper provides one of the first analyses of program search spaces using local optima networks. These are generated by sampling the search space by recording the progress of an Iterated Local Search algorithm. Source code mutations in comparison and Boolean operators are considered. The search spaces of two small benchmark programs, the triangle and TCAS programs, are analysed and visualised. Results show a high level of neutrality, i.e. connected test-equivalent mutants. It is also generally relatively easy to find a path from a random mutant to a mutant that passes all test cases.
Status: Book Chapter: author post-print (pre-copy editing)
Rights: © 2017 ACM. GECCO ’17 Companion, Berlin, Germany Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
URL: http://dx.doi.org/10.1145/3067695.3082518

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