- author = "F. O. {de Franca} and G. S. I. Aldeia",
- title = "Interaction-Transformation Evolutionary Algorithm for Symbolic Regression",
- journal = "Evolutionary Computation",
- year = "2021",
- volume = "29",
- number = "3",
- pages = "367--390",
- month = "Fall",
- keywords = "genetic algorithms, genetic programming, Symbolic Regression, Interaction-Transformation, ITEA, evolutionary algorithms",
- ISSN = "1063-6560",
-
DOI = "
doi:10.1162/evco_a_00285",
-
code_url = "
https://github.com/folivetti/ITEA/",
- size = "24 pages",
- abstract = "Interaction-Transformation (IT) is a new representation for Symbolic Regression that reduces the space of solutions to a set of expressions that follow a specific structure. The potential of this representation was illustrated in prior work with the algorithm called SymTree. This algorithm starts with a simple linear model and incrementally introduces new transformed features until a stop criterion is met. While the results obtained by this algorithm were competitive with the literature, it had the drawback of not scaling well with the problem dimension. This paper introduces a mutation only Evolutionary Algorithm, called ITEA, capable of evolving a population of IT expressions. One advantage of this algorithm is that it enables the user to specify the maximum number of terms in an expression. In order to verify the competitiveness of this approach, ITEA is compared to linear, nonlinear and Symbolic Regression models from the literature. The results indicate that ITEA is capable of finding equal or better approximations than other Symbolic Regression models while being competitive to state-of-the-art non-linear models. Additionally, since this representation follows a specific structure, it is possible to extract the importance of each original feature of a data set as an analytical function, enabling us to automate the explanation of any prediction. In conclusion, ITEA is competitive when comparing to regression models with the additional benefit of automating the extraction of additional information of the generated models.",
-
notes = "Fabricio Olivetti de Franca and Guilherme Seidyo Imai
Aldeia
See also https://arxiv.org/abs/1902.03983
Center for Mathematics, Computation and Cognition, Heuristics, Analysis and Learning Laboratory, Federal University of ABC, Santo Andre, Brazil",
