Name
Abstract | ||
|---|---|---|
Gene expression programming (GEP) is a new evolutionary algorithm based on genotype and phenotype, which is commonly used in network optimisation and prediction. Aiming at the problem that the traditional gene expression programming (GEP) is susceptible to noise interference, leading to premature convergence and falling into local solutions, this paper proposes an improved GEP algorithm, which increases u0027inverted seriesu0027 and u0027extractionu0027 operator. The improved algorithm can effectively increase the rate of utilisation of genes, with convergence speed and solution precision becoming higher, and can avoid the premature phenomenon. Taking the Chinese vegetables price change trend of mooli, scallion, white gourd, eggplant, green pepper and potato as example, this paper discusses the way to solve the forecast problems by adopting gene expression programming and constructing time interval unified time series data and normalised processing. Through the training and construction model, it realises the simulation ... |
Year | Venue | Field |
|---|---|---|
2018 | IJHPCN | Convergence (routing),Time series,Gene expression programming,Mathematical optimization,Evolutionary algorithm,Premature convergence,Computer science,Operator (computer programming),Distributed computing |
DocType | Volume | Issue |
Journal | 11 | 3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Yang | 1 | 194 | 37.52 |
| Kangshun Li | 2 | 38 | 4.28 |
| Wensheng Zhang | 3 | 0 | 1.01 |
| Yaolang Kong | 4 | 0 | 0.34 |