408 research outputs found
A divide and conquer method for symbolic regression
Symbolic regression aims to find a function that best explains the
relationship between independent variables and the objective value based on a
given set of sample data. Genetic programming (GP) is usually considered as an
appropriate method for the problem since it can optimize functional structure
and coefficients simultaneously. However, the convergence speed of GP might be
too slow for large scale problems that involve a large number of variables.
Fortunately, in many applications, the target function is separable or
partially separable. This feature motivated us to develop a new method, divide
and conquer (D&C), for symbolic regression, in which the target function is
divided into a number of sub-functions and the sub-functions are then
determined by any of a GP algorithm. The separability is probed by a new
proposed technique, Bi-Correlation test (BiCT). D&C powered GP has been tested
on some real-world applications, and the study shows that D&C can help GP to
get the target function much more rapidly
Elite Bases Regression: A Real-time Algorithm for Symbolic Regression
Symbolic regression is an important but challenging research topic in data
mining. It can detect the underlying mathematical models. Genetic programming
(GP) is one of the most popular methods for symbolic regression. However, its
convergence speed might be too slow for large scale problems with a large
number of variables. This drawback has become a bottleneck in practical
applications. In this paper, a new non-evolutionary real-time algorithm for
symbolic regression, Elite Bases Regression (EBR), is proposed. EBR generates a
set of candidate basis functions coded with parse-matrix in specific mapping
rules. Meanwhile, a certain number of elite bases are preserved and updated
iteratively according to the correlation coefficients with respect to the
target model. The regression model is then spanned by the elite bases. A
comparative study between EBR and a recent proposed machine learning method for
symbolic regression, Fast Function eXtraction (FFX), are conducted. Numerical
results indicate that EBR can solve symbolic regression problems more
effectively.Comment: The 2017 13th International Conference on Natural Computation, Fuzzy
Systems and Knowledge Discovery (ICNC-FSKD 2017
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