A greedy search tree heuristic for symbolic regression

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@Article{deFranca:2018:IS,
  author =       "Fabricio Olivetti {de Franca}",
  title =        "A greedy search tree heuristic for symbolic
                 regression",
  journal =      "Information Sciences",
  year =         "2018",
  volume =       "442",
  pages =        "18--32",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "https://doi.org/10.1016/j.ins.2018.02.040",
  DOI =          "doi:10.1016/j.ins.2018.02.040",
  publisher =    "Elsevier",
  abstract =     "Symbolic Regression tries to find a mathematical
                 expression that describes the relationship of a set of
                 explanatory variables to a measured variable. The main
                 objective is to find a model that minimizes the error
                 and, optionally, that also minimizes the expression
                 size. A smaller expression can be seen as an
                 interpretable model considered a reliable decision
                 model. This is often performed with Genetic
                 Programming, which represents their solution as
                 expression trees. The shortcoming of this algorithm
                 lies on this representation that defines a rugged
                 search space and contains expressions of any size and
                 difficulty. These pose as a challenge to find the
                 optimal solution under computational constraints. This
                 paper introduces a new data structure, called
                 Interaction-Transformation (IT), that constrains the
                 search space in order to exclude a region of larger and
                 more complicated expressions. In order to test this
                 data structure, it was also introduced an heuristic
                 called SymTree. The obtained results show evidence that
                 SymTree are capable of obtaining the optimal solution
                 whenever the target function is within the search space
                 of the IT data structure and competitive results when
                 it is not. Overall, the algorithm found a good
                 compromise between accuracy and simplicity for all the
                 generated models.",
}

Genetic Programming entries for Fabricio Olivetti de Franca

Citations