文件列表:

GA_framework\GA_framework\.dropbox (10, 2012-07-03)
GA_framework\GA_framework\.DS_Store (12292, 2012-10-22)
GA_framework\GA_framework\mn_adaptation_individual.m (969, 2012-09-11)
GA_framework\GA_framework\mn_adaptation_populational.m (890, 2012-09-11)
GA_framework\GA_framework\mn_evaluate_population.m (5778, 2012-10-22)
GA_framework\GA_framework\mn_GA.m (5556, 2012-09-12)
GA_framework\GA_framework\mn_GA_gui.fig (260488, 2012-09-11)
GA_framework\GA_framework\mn_GA_gui.m (70018, 2012-09-12)
GA_framework\GA_framework\mn_initialize_functions.m (1964, 2012-09-12)
GA_framework\GA_framework\mn_initialize_population.m (845, 2012-09-12)
GA_framework\GA_framework\mn_initialize_settings.m (1907, 2012-10-22)
GA_framework\GA_framework\mn_initialize_settings_gui.fig (488400, 2012-10-22)
GA_framework\GA_framework\mn_initialize_settings_gui.m (50277, 2012-10-22)
GA_framework\GA_framework\mn_mop_piceag.m (2788, 2012-10-22)
GA_framework\GA_framework\mn_mop_scalar.m (903, 2012-09-12)
GA_framework\GA_framework\mn_reproduction.m (1813, 2012-09-10)
GA_framework\GA_framework\mn_scaling_linear.m (1064, 2012-09-09)
GA_framework\GA_framework\mn_scaling_rank.m (731, 2012-09-09)
GA_framework\GA_framework\mn_scaling_sigma.m (626, 2012-09-09)
GA_framework\GA_framework\mn_selection_roulette.m (1162, 2012-09-09)
GA_framework\GA_framework\mn_selection_srs.m (2388, 2012-09-09)
GA_framework\GA_framework\mn_selection_tournament.m (875, 2012-09-09)
GA_framework\GA_framework\mn_survival.m (1903, 2012-09-12)
GA_framework\GA_framework\mn_update_settings.m (5328, 2012-09-11)
GA_framework\GA_framework\sample_problems\.DS_Store (6148, 2012-10-22)
GA_framework\GA_framework\sample_problems\knapsack\knapsack_crossover_1point.m (404, 2012-09-02)
GA_framework\GA_framework\sample_problems\knapsack\knapsack_evaluate.m (249, 2012-09-02)
GA_framework\GA_framework\sample_problems\knapsack\knapsack_generate_random.m (172, 2012-09-02)
GA_framework\GA_framework\sample_problems\knapsack\knapsack_initialize.m (1069, 2012-09-02)
GA_framework\GA_framework\sample_problems\knapsack\knapsack_mutation_bitflop.m (136, 2012-09-02)
GA_framework\GA_framework\sample_problems\knapsack\knapsack_print.m (613, 2012-09-11)
GA_framework\GA_framework\sample_problems\mop\mop_crossover_polarized.m (1244, 2012-09-12)
GA_framework\GA_framework\sample_problems\mop\mop_evaluate.m (144, 2012-09-12)
GA_framework\GA_framework\sample_problems\mop\mop_evaluate_bk1.m (162, 2012-09-12)
GA_framework\GA_framework\sample_problems\mop\mop_generate_random.m (224, 2012-09-12)
GA_framework\GA_framework\sample_problems\mop\mop_initialize.m (1430, 2012-09-12)
GA_framework\GA_framework\sample_problems\mop\mop_mutation_gaussian.m (308, 2012-09-12)
GA_framework\GA_framework\sample_problems\mop\mop_print.m (552, 2012-09-12)
GA_framework\GA_framework\sample_problems\mop\realfunction_reduceinterval.m (315, 2011-09-30)
... ...

THE IS A SAMPLE MULTIOBJECTIVE PROBLEM THAT DEPENDS ON THE PARETOFRONTS FUNCTION AVAILABLE ON: http://www.mathworks.com/matlabcentral/fileexchange/37080-pareto-fronts-according-to-dominance-relation In this folder we have a multiobjective optimization problem. The GA framework will use a PICEA-g (2012) algorithm to solve it Wang, R., Purshouse, R. C., & Fleming, P. J. (2012) Preference-inspired Co-evolutionary Algorithms for Many-objective Optimisation PICEA-g is a co-evolutinary algorithm. A second auxiliar population of vectors is needed to solve the main multiobjective problem. This second population is kept and updated as part of the problem structure while the main population is the one returned by the algorithm. The fitness assignment depends on both objective function values so you can't use the simple fitness scaling methods for monoobjective optimization because the fitness assigment method is what makes the PICEA-g special. Apart from that, in PICEA-g, all the children and parents compete for survival together with a 100% elitism. So that also has to be changed in the settings of the GA.

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