title: Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem.
abstract: A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR.
authors.affiliation: Department of Computer Science and Technology, China University of Petroleum, Beijing 102249, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Lu
authors.affiliation: Department of Computer Science and Technology, China University of Petroleum, Beijing 102249, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Ren
authors.affiliation: Department of Computer Science and Technology, China University of Petroleum, Beijing 102249, China.
authors.forename: Zhiguang
authors.initials: Z
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008957:Models, Genetic; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D012044:Regression Analysis
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