Name
Abstract | ||
|---|---|---|
Evolutionary programming (EP) is a classical evolutionary algorithm for continuous optimization. There have been several EP algorithms proposed based on different mutations strategies like Gaussian, Cauchy, Levy and other stochastic distributions. However, their convergence speed should be improved. An EP based on individual random difference (EP-IRD) was proposed to attain better solutions in a higher speed. The mutation of EP-IRD uses a random difference of individuals selected randomly to update the variance with which offspring are generated. The IRD-based mutation can make the better offspring according to the current population faster than the mathematical stochastic distribution. The numerical results of solving benchmark problems indicate that EP-IRD performs better than other four EP algorithms based on mathematical stochastic distribution in the items of convergence speed, optimal value on average and standard deviation. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-17563-3_41 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
evolutionary programming,standard deviation,continuous optimization,evolutionary algorithm | Population,Human-based evolutionary computation,Evolutionary algorithm,Genetic programming,Cauchy distribution,Artificial intelligence,Evolutionary programming,Genetic algorithm,Mathematical optimization,Evolutionary computation,Algorithm,Machine learning,Mathematics | Conference |
Volume | ISSN | Citations |
6466 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaoquan Cai | 1 | 52 | 12.23 |
| Han Huang | 2 | 159 | 30.23 |
| Zhifeng Hao | 3 | 653 | 78.36 |
| Xueqiang Li | 4 | 47 | 4.54 |