Dimensionally Constrained Symbolic Regression

Suyong Choi

Dimensionally Constrained Symbolic Regression

Machine Learning 2011-06-21 v1 Neural and Evolutionary Computing Computational Physics

Authors: Suyong Choi

Abstract

We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraining the terms allowed in the symbolic regression, convergence behavior is improved. Dimensionally constrained symbolic regression (DCSR) finds solutions with much better fitness than is normally possible with symbolic regression. In some cases, novel solutions are found.

Keywords

sufficient dimension reduction sampling algorithms statistical data analysis

Cite

@article{arxiv.1106.3834,
  title  = {Dimensionally Constrained Symbolic Regression},
  author = {Suyong Choi},
  journal= {arXiv preprint arXiv:1106.3834},
  year   = {2011}
}

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