Algorithm 2 Genetic-Based Feature Selection Method
	Input:F (number of features), G (number of generations), P (number of participants)
1:	forf = 1 to F
2:	Make a random population that all individuals have f ones
3:	forg =1 to G
4:	forp = 1 to P
5:	Train the regressor on data of all participants except participant p
6:	Calculate R2 error of the regressor on the participant p
7:	end for
8:	Fitness= average of R2 errors
9:	Generating Mating pool
10:	Cross over
11:	Mutation
12:	Elitism
13:	Produce the new generation
14:	end for
15:	output best chromosome
16:	end for