Name
Abstract | ||
|---|---|---|
Evolvable hardware (EHW) is facing the problems of scalability. Evolutionary algorithms often trap into local optima, or stalling in the later procedure. This paper analyses the difficulty of EHW. To improve the efficiency of Cartesian Genetic Programming (CGP), Neighborhood searching and orthogonal experiment design are tailed to an orthogonal mutation operator and a new Orthogonal Cartesian Genetic Programming algorithm is proposed. Demonstrated by experiments on the benchmark, the proposed Orthogonal Cartesian Genetic Programming can jump out of Local optima and decrease the stalling effect. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IIKI.2014.52 | IIKI |
Keywords | Field | DocType |
Evolvable hardware, Cartesian Genetic Programming, orthogonal experiment design, Evolutionary algorithm | Evolutionary algorithm,Computer science,Local optimum,Algorithm,Evolvable hardware,Genetic programming,Genetic representation,Evolutionary programming,Scalability,Design of experiments | Conference |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuchuan Ni | 1 | 0 | 0.34 |
| Yuanxiang Li | 2 | 245 | 51.20 |
| Xiaoyan Yang | 3 | 9 | 5.20 |
| Fuchuan Ni | 4 | 0 | 0.34 |
| Jinhai Xiang | 5 | 17 | 3.30 |