Visualising the global structure of search landscapes: genetic improvement as a case study

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@Article{Veerapen:2018:GPEM,
  author =       "Nadarajen Veerapen and Gabriela Ochoa",
  title =        "Visualising the global structure of search landscapes:
                 genetic improvement as a case study",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2018",
  volume =       "19",
  number =       "3",
  pages =        "317--349",
  month =        sep,
  note =         "Special issue on genetic programming, Genetic
                 improvement, Fitness landscape, Local optima network,
                 Visualisation",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1389-2576",
  URL =          "http://hdl.handle.net/1893/27485",
  URL =          "http://hdl.handle.net/11667/120",
  URL =          "https://doi.org/10.1007/s10710-018-9328-1",
  DOI =          "doi:10.1007/s10710-018-9328-1",
  size =         "33 pages",
  abstract =     "The search landscape is a common metaphor to describe
                 the structure of computational search spaces. Different
                 landscape metrics can be computed and used to predict
                 search difficulty. Yet, the metaphor falls short in
                 visualisation terms because it is hard to represent
                 complex landscapes, both in terms of size and
                 dimensionality. This paper combines local optima
                 networks, as a compact representation of the global
                 structure of a search space, and dimensionality
                 reduction, using the t-distributed stochastic neighbour
                 embedding algorithm, in order to both bring the
                 metaphor to life and convey new insight into the search
                 process. As a case study, two benchmark programs, under
                 a genetic improvement bug-fixing scenario, are analysed
                 and visualised using the proposed method. Local optima
                 networks for both iterated local search and a hybrid
                 genetic algorithm, across different neighbourhoods, are
                 compared, highlighting the differences in how the
                 landscape is explored.",
  notes =        "Triangle, TCAS, LON, t-SNE Research Data
                 http://hdl.handl​e.net/11667​/120",
}

Genetic Programming entries for Nadarajen Veerapen Gabriela Ochoa

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