Abstract-Genetic programming is an evolutionary algorithm, which allows performing symbolic regression -the important task of obtaining the analytical form of a model by the data, produced by the model. One of the known problems of genetic programming is expressions' bloating that results in ineffictevely long expressions. To prevent bloating, symbolic simplification of expression is used. We introduce a new approach to simplification in genetic programming, making it a uniform part of the evolutionary process. To do that, we develop a genetic programming on the basis of transofmation rules, similarly to computer algebra systems. We compare our approach with existed solution, and prove its adequacy and effectiviness.

Genetic programming is a methodology, widely used in data mining for obtaining the analytic form that describes a given experimental data set. In some cases, genetic programming is complemented by symbolic computations that simplify found expressions. We propose to unify the induction of genetic programming with the deduction of symbolic computations in one genetic algorithm. Our approach was implemented as the .NET library and successfully tested at various data mining problems: function approximation, invariants finding and classification.