Visualising the Search Landscape of the Triangle Program

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "William B. Langdon and Nadarajen Veerapen and 
                 Gabriela Ochoa",
  title =        "Visualising the Search Landscape of the Triangle
  booktitle =    "EuroGP 2017",
  year =         "2017",
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  volume =       "10196",
  series =       "LNCS",
  pages =        "96--113",
  address =      "Amsterdam",
  month =        "19-21 " # apr,
  organisation = "species",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, software~engineering, SBSE, heuristic
                 methods, test equivalent higher order mutants",
  URL =          "",
  DOI =          "doi:10.1007/978-3-319-55696-3_7",
  size =         "16 pages",
  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.",
  notes =        "code

                 Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and

Genetic Programming entries for William B Langdon Nadarajen Veerapen Gabriela Ochoa