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Table 3 Fitness and sensitivity obtained from different settings.

From: Reverse engineering gene regulatory networks: Coupling an optimization algorithm with a parameter identification technique

  dataset 1 dataset 2 dataset 1 dataset 2
  w/o with w/o with w/o with w/o with
Avg 2.02E-03 1.17E-03 8.69E-02 4.62E-02 4.34E-04 1.45E-04 3.04E-02 1.23E-02
Best 2.97E-04 5.79E-04 1.76E-02 1.39E-02 2.47E-04 4.90E-05 2.42E-03 6.66E-04
Worst 5.50E-03 2.72E-03 2.46E-01 1.42E-01 1.57E-03 4.62E-04 9.51E-02 5.76E-02
S.D. 1.79E-03 5.48E-04 6.42E-02 2.98E-02 2.45E-04 9.56E-05 3.29E-02 1.70E-02
Sensitivity 0.7703 0.7281 0.6929 0.6468 0.7844 0.7388 0.7684 0.7462