Algorithm2. Overall framework of the IGEP-based offline training method.

1: Inputs: Training instances I, $\forall i=1,2,\dots ,n$ 2: Set parameters, population size (popsize), maximum iteration (maxIter), probability of mutation (pm), probability of recombination (pr), probability of transposition (ptr), and the pre-set value for selection (k_s). 3: Initialize population randomly P, $\forall j=1,2,\dots ,\mathit{popsize}$ 4: Calculate each individuals Fj, store the best individual into Jbest 5: for iteration = 1: maxIter do 6:  Apply selection to generate the new population 7:  if p < pm 8:   Apply recombination to generate the new population 9:  end if 10:  if p < pm 11:   Apply mutation to generate the new population 12:  end if 13:  if p < pm 14:   Apply transposition to generate the new population 15:  end if 16:  Calculate each individuals Fj, store the best individual into Jbest 17:  $iteration\leftarrow iteration+1$ 18: end for 19: Compare Jbest with the dispatching rule JR_base in the rules base by testing instances. 20: if Jbest is better than JR_base then 21: JR_base =Jbest 22: end if