Modelling Genetic Improvement Landscapes with Local Optima Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Veerapen:2017:GI,

• author = "Nadarajen Veerapen and Fabio Daolio and Gabriela Ochoa",
• title = "Modelling Genetic Improvement Landscapes with Local Optima Networks",
• booktitle = "GI-2017",
• year = "2017",
• editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer",
• pages = "1543--1548",
• address = "Berlin",
• month = "15-19 " # jul,
• publisher = "ACM",
• note = "best presentation prize",
• keywords = "genetic algorithms, genetic programming, genetic improvement, fitness landscape, Local Optima Network, iterated local search, ILS, multiple hill climber",
• isbn13 = "978-1-4503-4939-0",
• URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/veerapen2017_local_optima_networks.pdf",
• DOI = "doi:10.1145/3067695.3082518",
• size = "6 pages",
• 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 case",
• notes = "triangle_comparison_ops.zip etc in http://hdl.handle.net/11667/89 Mutation of comparisons and Boolean operators super mutant, libtooling Clang-LLVM, escape edges, igraph",

}

Genetic Programming entries for Nadarajen Veerapen Fabio Daolio Gabriela Ochoa

Citations