Table 7.

Parameters setting

Algorithms	Parameters
NSGA-II	Population size: 100
	Selection: binary tournament selection
	Crossover: single point crossover, $\text{pc}=0.9$
	$\text{Number of generation}=20$
	Mutation: polynomial mutation, $\text{pm}=0.1$
Genetic algorithm	Population size: 100
	Mutation: 0.1
	Crossover: 0.9
	$\text{Number of generation}=20$
AdaBoost (dataset1)	$\text{Number of estimators}=177$
	Max_depth=6
	$\text{Learning rate}=0.002$
AdaBoost (dataset2)	$\text{Number of estimators}=99$
	Max_depth=11
	$\text{Learning rate}=0.022$