Zonglin Jiang

Zonglin Jiang
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Zonglin Jiang
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Computer Science - Neural and Evolutionary Computing (3)
 
Computer Science - Data Structures and Algorithms (1)

Publications Authored By Zonglin Jiang

Symbolic regression that aims to detect underlying data-driven model has become increasingly important for industrial data analysis. For most of existing algorithms, such as genetic programming (GP), the convergence speed might be too slow for large scale problems with a large number of variables. This situation may become even worse with increasing problem size. Read More

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. Read More

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. Read More