Nikola Gligorovski
ABSTRACT
Symbolic regression (SR) is a well-known regression problem, that aims to find a symbolic expression that best fits a given dataset. Linear Genetic Programming (LGP) is a good and powerful candidate for solving symbolic regression problems. However, current LGPs for SR only focus on finding scalar-valued functions, and limited work has been done on finding vector-valued functions with vectorial-based LGP. In addition, a comprehensive dataset for testing vectorial-based GP is still lacking in the literature. To this end, we propose a new extensive benchmark suite for vectorial symbolic regression. Furthermore, we propose a new vectorial LGP algorithm for symbolic regression, which directly deals with high dimensional data using vectorial representation and operations. Experimental results show that the proposed algorithm outperforms another recently published vectorial GP method on the benchmark suite for vector-valued functions and that it also generalizes better on unseen data.
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Index Terms
- LGP-VEC: A Vectorial Linear Genetic Programming for Symbolic Regression
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