lower_bounds = np.repeat(-1, player_char_num + team_char_num + schedule_char_num) ###### # Create initial population logging.info('Initializing Population') population = GA_Funs.create_population(lower_bounds, upper_bounds, n=N_pop, type_fill='random') ###### # Assess initial fitness fitness = np.repeat(0.0, N_pop) for n in range(N_pop): fitness[n] = GA_Funs.get_fitness(population[n,], schedule_data, team_data1, team_data2, team_cols1, team_cols2, player_data1, player_data2, player_cols1, player_cols2, health_data) ###### # Start Genetic Algorithm logging.info('Starting Genetic Algorithm') fitness_plot_vals = np.repeat(0.0, N_gen) for g in range(N_gen): tic = timeit.default_timer() print('Calculating Generation #' + str(g+1)) # Order array fitness fitness_ordered_ind = sorted(range(len(fitness)), key=lambda k: fitness[k], reverse=True)
###### # Create initial population logging.info('Initializing Population') population = GA_Funs.create_population(lower_bounds, upper_bounds, n=N_pop, type_fill='random') ###### # Assess initial fitness fitness = np.repeat(0.0, N_pop) for n in range(N_pop): fitness[n] = GA_Funs.get_fitness(population[n, ], schedule_data, team_data1, team_data2, team_cols1, team_cols2, player_data1, player_data2, player_cols1, player_cols2, health_data) ###### # Start Genetic Algorithm logging.info('Starting Genetic Algorithm') fitness_plot_vals = np.repeat(0.0, N_gen) for g in range(N_gen): tic = timeit.default_timer() print('Calculating Generation #' + str(g + 1)) # Order array fitness fitness_ordered_ind = sorted(range(len(fitness)), key=lambda k: fitness[k],