Automated Discovery of Numerical Approximation Formulae via Genetic Programming
Genetic Programming and Evolvable Machines
Solving differential equations with genetic programming
Genetic Programming and Evolvable Machines
Grammatical Swarm: The generation of programs by social programming
Natural Computing: an international journal
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Improving symbolic regression with interval arithmetic and linear scaling
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
On the importance of data balancing for symbolic regression
IEEE Transactions on Evolutionary Computation
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
Low dimensional simplex evolution: a new heuristic for global optimization
Journal of Global Optimization
IEEE Transactions on Evolutionary Computation
Some modifications of low-dimensional simplex evolution and their convergence
Optimization Methods & Software
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Data-driven model is highly desirable for industrial data analysis in case the experimental model structure is unknown or wrong, or the concerned system has changed. Symbolic regression is a useful method to construct the data-driven model (regression equation). Existing algorithms for symbolic regression such as genetic programming and grammatical evolution are difficult to use due to their special target programming language (i.e., LISP) or additional function parsing process. In this paper, a new evolutionary algorithm, parse-matrix evolution (PME), for symbolic regression is proposed. A chromosome in PME is a parse-matrix with integer entries. The mapping process from the chromosome to the regression equation is based on a mapping table. PME can easily be implemented in any programming language and free to control. Furthermore, it does not need any additional function parsing process. Numerical results show that PME can solve the symbolic regression problems effectively.