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. Genetic Improvement Workshop 2017, Berlin, Germany, 15.07.2017-15.07.2017. New York: ACM, pp. 1543-1548. http://dx.doi.org/10.1145/3067695.3082518; https://doi.org/10.1145/3067695.3082518

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.

Keywords
Fitness landscape; Local Optima Network; Genetic Improvement

People

Professor Gabriela Ochoa

Professor, Computing Science