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Paper detail

Dimensionally Constrained Symbolic Regression

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.

preprint2011arXivOpen access
Suyong Choi
Neural and Evolutionary Computingphysics.comp-phMachine Learning
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Suyong Choi

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