Understanding grammatical evolution: initialisation

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  author =       "Miguel Nicolau",
  title =        "Understanding grammatical evolution: initialisation",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2017",
  volume =       "18",
  number =       "4",
  pages =        "467--507",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution Initialisation Representation bias Tree
                 creation Symbolic regression Classification Design",
  ISSN =         "1389-2576",
  URL =          "https://link.springer.com/article/10.1007/s10710-017-9309-9",
  DOI =          "doi:10.1007/s10710-017-9309-9",
  size =         "41 pages",
  abstract =     "Grammatical evolution is one of the most used variants
                 of genetic programming, and ever since its
                 introduction, several improvements have been suggested.
                 One of these concerns the routine used to create the
                 initial population. In this study, several proposed
                 initialisation routines are compared; based on a
                 detailed analysis of the generated initial populations,
                 and subsequent results obtained on a large set of
                 experiments, a variant of the PTC2 algorithm is shown
                 to consistently outperform all other routines, while a
                 variant of random initialisation provides a good
                 compromise between efficiency and ease of

Genetic Programming entries for Miguel Nicolau