Prudent alignment and crossover of decision trees in genetic programming

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  author =       "Matej Sprogar",
  title =        "Prudent alignment and crossover of decision trees in
                 genetic programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2015",
  volume =       "16",
  number =       "4",
  pages =        "499--530",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Decision
                 trees, Crossover, Context, Alignment",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-015-9243-7",
  size =         "32 pages",
  abstract =     "Crossover is the central search operator responsible
                 for navigating through unknown problem landscapes while
                 at the same time the main conservation operator, which
                 is supposed to preserve the already learnt lessons.
                 This paper is about a novel homologous decision tree
                 crossover operator. Contrary to other tree crossover
                 operators it defines the context for a decision tree
                 node and elaborates a fast one-sample-based tree
                 alignment procedure. The idea is to replace a sub-tree
                 with a better one from the same context, as defined by
                 the decision tree training process. This operator does
                 not rely on the topological properties of the tree but
                 rather on its behavioural properties. During empirical
                 testing the new operator showed the best generalisation

Genetic Programming entries for Matej Sprogar