Using Matlab multi-objective evolutionary algorithm to achieve the source, the user can define the parameters of the corresponding

NSGA2\NSGA2\binary_decode.m
.....\.....\binary_mutation.m
.....\.....\blend_crossover.m
.....\.....\compare_crow_dist.m
.....\.....\compare_rank_crow.m
.....\.....\compute_obj_error.m
.....\.....\compute_sp.asv
.....\.....\compute_sp.m
.....\.....\constr_computation.m
.....\.....\crossover.m
.....\.....\crowding_distance_assignment.asv
.....\.....\crowding_distance_assignment.m
.....\.....\crowding_distance_truncated_assignment.m
.....\.....\crtbase.M
.....\.....\crtbp.M
.....\.....\crtrp.M
.....\.....\decode.m
.....\.....\detail.txt
.....\.....\display_func.m
.....\.....\elitism.m
.....\.....\generate_cites.m
.....\.....\generate_P_pop.m
.....\.....\gray2bin.m
.....\.....\GUI.fig
.....\.....\GUI.m
.....\.....\ind_cmp.m
.....\.....\ind_cmp_constr.m
.....\.....\init_P_ranking.m
.....\.....\linear_crossover.m
.....\.....\main_loop.m
.....\.....\multipoint_crossover.m
.....\.....\mutation.m
.....\.....\NSGA_sorting.asv
.....\.....\NSGA_sorting.m
.....\.....\obj_computation.m
.....\.....\polynomial_mutation.m
.....\.....\print_generation.m
.....\.....\random_mutation.m
.....\.....\result.txt
.....\.....\simulated_binary_crossover.m
.....\.....\tournament_selection.m
.....\.....\uniform_crossover.m
.....\NSGA2
NSGA2

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