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This paper investigates the problem of bloat in Symbolic Regression (SR). It develops a procedure to simplify superpositions generated by SR. The present approach borrows ideas of equivalent decision simplification and they are applied to create parametric rules of rewriting. Except from eliminating excessive parts of superpositions, these rules reduce dimensionality of parameter space of generated superpositions. Computational experiment is conducted on a dataset related to Brent Crude Oil options. There, the volatility of options prices is approximated by its strike prices and expiration date.

Creation of parametric rules to rewrite algebraic expressions in Symbolic Regression