Table 5 Performance of classical vs multi objective real-coded GA over 20 runs using the 2-gene synthetic dataset
From: Comparison of evolutionary algorithms in gene regulatory network model inference
Criteria | CLGA | MOGA | Fuzzy MOGA |
|---|---|---|---|
Goodness of data fit (Best/Average SE) | 0.04115/0.209183826 | 0.023202753/0.14008799 | 0.019817555/0.10705668 |
Parameter quality (Best/Average SE) | 2.368969422/10.25508788 | 1.138802292/11.22558038 | 1.685895101/9.676239684 |
Robustness (Kinetic orders/Rate constants variance) | 0.324876643/1.107014477 | 0.320798581/4.085473222 | 0.279334243/1.518127766 |
Average running time | 187.6s | 302.8s | 300.6s |