#!/usr/bin/env python # ------------------------------------------------------------------------------------------------------% # Created by "Thieu Nguyen" at 10:11, 16/03/2020 % # % # Email: [email protected] % # Homepage: https://www.researchgate.net/profile/Thieu_Nguyen6 % # Github: https://github.com/thieunguyen5991 % #-------------------------------------------------------------------------------------------------------% from opfunu.type_based.uni_modal import Functions from mealpy.evolutionary_based.GA import BaseGA t1 = Functions() root_paras = { "problem_size": 30, "domain_range": [-15, 15], "print_train": True, "objective_func": t1._sum_squres__ } ## Setting parameters epoch = 100 pop_size = 50 pc = 0.95 pm = 0.025 md = BaseGA(root_paras, epoch, pop_size, pc, pm) best_position, best_fit, list_loss = md._train__() print(best_fit)
#!/usr/bin/env python # ------------------------------------------------------------------------------------------------------% # Created by "Thieu Nguyen" at 22:08, 22/05/2020 % # % # Email: [email protected] % # Homepage: https://www.researchgate.net/profile/Thieu_Nguyen6 % # Github: https://github.com/thieunguyen5991 % #-------------------------------------------------------------------------------------------------------% from mealpy.evolutionary_based.GA import BaseGA from opfunu.cec_basic.cec2014_nobias import * ## Setting parameters objective_func = F1 problem_size = 100 domain_range = [-100, 100] log = True epoch = 100 pop_size = 50 md1 = BaseGA(objective_func, problem_size, domain_range, log, epoch, pop_size) best_pos1, best_fit1, list_loss1 = md1._train__() print(best_fit1)