Description: onventional optimization algorithms using linear and non-linear programming sometimes have difficulty in finding the global optima or in case of multi-objective optimization, the pareto front. A lot of research has now been directed towards evolutionary algorithms (genetic algorithm, particle swarm optimization etc) to solve multi objective optimization problems.
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File list (Check if you may need any files):
example of NSGA\crowding_distance.m
...............\evaluate_objective.m
...............\genetic_operator.m
...............\html\crowding_distance.html
...............\....\genetic_operator.html
...............\....\initialize_variables.html
...............\....\non_domination_sort_mod.html
...............\....\nsga_2.html
...............\....\replace_chromosome.html
...............\....\tournament_selection.html
...............\initialize_variables.m
...............\non_domination_sort_mod.m
...............\nsga_2.asv
...............\nsga_2.m
...............\NSGA_2.pdf
...............\plot_objective.m
...............\replace_chromosome.m
...............\solution.txt
...............\tournament_selection.m
...............\html
example of NSGA
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