A greedy search tree heuristic for symbolic regression
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- @Article{deFranca:2018:IS,
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author = "Fabricio {Olivetti de Franca}",
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title = "A greedy search tree heuristic for symbolic
regression",
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journal = "Information Sciences",
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year = "2018",
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volume = "442",
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pages = "18--32",
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month = may,
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keywords = "genetic algorithms, genetic programming",
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URL = "https://doi.org/10.1016/j.ins.2018.02.040",
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DOI = "doi:10.1016/j.ins.2018.02.040",
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publisher = "Elsevier",
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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