Name
Abstract | ||
|---|---|---|
Gene Expression Programming (GEP) is a powerful evolutionary method for knowledge discovery and model learning. Based on the basic GEP algorithm, this paper proposes an improved algorithm named S_GEP, which is especially suitable for dealing with symbolic regression problems. The major advantages for this S_GEP method include: (1) A new method for evaluating individual without expression tree; (2) a corresponding expression tree construction schema for the new evaluating individual method if required by some special complex problems; and (3) a new approach for manipulating numeric constants so as to improve the convergence. A thorough comparative study between our proposed S_GEP method with the primitive GEP, as well as other methods are included in this paper. The comparative results show that the proposed S_GEP method can significantly improve the GEP performance. Several well-studied benchmark test cases and real-world test cases demonstrate the efficiency and capability of our proposed S_GEP for symbolic regression problems. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2013.05.062 | Neurocomputing |
Keywords | Field | DocType |
Genetic computing,Gene Expression Programming,Evolutionary algorithm,Symbolic regression,Data modeling | Convergence (routing),Gene expression programming,Data modeling,Evolutionary algorithm,Computer science,Test case,Artificial intelligence,Knowledge extraction,Symbolic regression,Binary expression tree,Machine learning | Journal |
Volume | ISSN | Citations |
137 | 0925-2312 | 9 |
PageRank | References | Authors |
0.63 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-zhong Peng | 1 | 10 | 1.66 |
| Chang-an Yuan | 2 | 85 | 9.88 |
| Xiao Qin | 3 | 9 | 0.97 |
| JiangTao Huang | 4 | 9 | 0.63 |
| YaBing Shi | 5 | 9 | 0.63 |