I want to solve a symbolic regression problem with genetic programming. My dataset is similar to this one, but I have 30 features, and I want to use only the most sensitive features. I found this library interesting for Symbolic Regression, but could not find the right approach for feature selection.

  • $\begingroup$ Have you tried to look at the existing feature selection techniques typically used in machine learning? If yes, why aren't they good for you? If not, it may be a good idea to have a look at them. Once you know the answer to your question, feel free to write it below (if it's good ehough) ;) $\endgroup$ – nbro Mar 7 at 11:20
  • $\begingroup$ You should probably read this: Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression. $\endgroup$ – nbro Mar 10 at 15:50
  • $\begingroup$ I do not have full access to that paper, but it claims as follows, In this paper, we propose a new feature selection method based on permutation to select features for high-dimensional SR using GP. So, permutation using GP. what's it really? $\endgroup$ – thunder Mar 10 at 16:46
  • $\begingroup$ You can find a freely available version of this paper (or a draft) on the web. $\endgroup$ – nbro Mar 10 at 16:47
  • $\begingroup$ Is there a python library for GP? $\endgroup$ – thunder Mar 10 at 17:00

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