Name
Abstract | ||
|---|---|---|
Gene expression programming (GEP) is a functional genotype/phenotype system. The separation scheme increases the efficiency and reliability of GEP. However, the computational cost increases considerably with the expansion of the scale of problems. In this paper, we introduce a GPU-accelerated hybrid variant of GEP named pGEP (parallel GEP). In order to find the optimal constant coefficients locally on the fixed function structure, the Method of Least Square (MLS) has been embedded into the GEP evolutionary process. We tested pGEP using a broad problem set with a varying number of instances. In the performance experiment, the GPU-based GEP, when compared with the traditional GEP version, increased speeds by approximately 250 times. We compared pGEP with other well-known constant creation methods in terms of accuracy, demonstrating MLS performs at several orders of magnitude higher in terms of both the best residuals and average residuals. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2330163.2330302 | GECCO |
Keywords | Field | DocType |
broad problem,well-known constant creation method,best residual,traditional gep version,average residual,enhanced gep algorithm,gpu-based gep,gep evolutionary process,gpu-accelerated hybrid variant,parallel gep,optimal constant coefficient,gpu-based implementation,gene expression programming,least square | Least squares,Gene expression programming,Mathematical optimization,Fixed-function,Computer science,CUDA,Constant coefficients,Algorithm,Symbolic regression | Conference |
Citations | PageRank | References |
5 | 0.51 | 3 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuai Shao | 1 | 5 | 0.51 |
| Xiyang Liu | 2 | 159 | 18.55 |
| Mingyuan Zhou | 3 | 631 | 52.76 |
| Jiguo Zhan | 4 | 5 | 0.51 |
| Xin Liu | 5 | 3919 | 320.56 |
| Yanli Chu | 6 | 5 | 0.51 |
| Hao Chen | 7 | 2723 | 183.89 |