#!/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)
from opfunu.cec_basic.cec2014_nobias import *
from mealpy.evolutionary_based.GA import BaseGA
## Setting parameters
obj_func = F5
verbose = False
epoch = 500
pop_size = 50
# A - Different way to provide lower bound and upper bound. Here are some examples:
## 1. When you have different lower bound and upper bound for each parameters
lb1 = [-3, -5, 1, -10]
ub1 = [5, 10, 100, 30]
md1 = BaseGA(obj_func, lb1, ub1, verbose, epoch, pop_size)
best_pos1, best_fit1, list_loss1 = md1.train()
print(md1.solution[1])
## 2. When you have same lower bound and upper bound for each parameters, then you can use:
## + int or float: then you need to specify your problem size (number of dimensions)
problemSize = 10
lb2 = -5
ub2 = 10
md2 = BaseGA(obj_func,
lb2,
ub2,
verbose,
epoch,
pop_size,
problem_size=problemSize) # Remember the keyword "problem_size"
# ------------------------------------------------------------------------------------------------------% # Created by "Thieu Nguyen" at 09:33, 17/03/2020 % # % # Email: [email protected] % # Homepage: https://www.researchgate.net/profile/Thieu_Nguyen6 % # Github: https://github.com/thieunguyen5991 % # -------------------------------------------------------------------------------------------------------% from numpy import sum from mealpy.evolutionary_based.GA import BaseGA def my_objective_function(solution): return sum(solution**2) ## Setting parameters objective_func = my_objective_function problem_size = 30 domain_range = [-15, 15] log = True epoch = 100 pop_size = 50 pc = 0.95 pm = 0.025 md = BaseGA(objective_func, problem_size, domain_range, log, epoch, pop_size, pc, pm) best_position, best_fit, list_loss = md._train__() print(best_fit)
t3 += (1 + solution[i]**2)**0.5
return [t1, t2, t3]
## Setting parameters
verbose = True
epoch = 100
pop_size = 50
lb1 = [-10, -5, -15, -20]
ub1 = [10, 5, 15, 20]
optimizer = BaseGA(obj_function,
lb1,
ub1,
"max",
verbose,
epoch,
pop_size,
obj_weight=[0.2, 0.5, 0.3])
best_position, best_fitness, g_best_fit_list, c_best_fit_list = optimizer.train(
)
print(best_position)
export_convergence_chart(
optimizer.history_list_g_best_fit, title='Global Best Fitness'
) # Draw global best fitness found so far in previous generations
export_convergence_chart(
optimizer.history_list_c_best_fit, title='Local Best Fitness'
) # Draw current best fitness in each previous generation
export_convergence_chart(optimizer.history_list_epoch_time,
title='Runtime chart',
#!/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/thieu1995 % #-------------------------------------------------------------------------------------------------------% from opfunu.cec_basic.cec2014_nobias import * from mealpy.evolutionary_based.GA import BaseGA ## Setting parameters obj_func = F5 verbose = False epoch = 10 pop_size = 50 lb1 = [-3, -5, 1, -10] ub1 = [5, 10, 100, 30] md1 = BaseGA(obj_func, lb1, ub1, verbose, epoch, pop_size) best_pos1, best_fit1, list_loss1 = md1.train() print(md1.solution[1])
export_diversity_chart, export_objectives_chart, export_trajectory_chart
from numpy import array
## Setting parameters
problem1 = {
"obj_func": F5,
"lb": [-3, -5, 1, -10],
"ub": [5, 10, 100, 30],
"minmax": "min",
"verbose": True,
}
# A - Different way to provide lower bound and upper bound. Here are some examples:
## 1. When you have different lower bound and upper bound for each parameters
md1 = BaseGA(problem1, epoch=10, pop_size=50)
best_pos1, best_fit1 = md1.train()
print(md1.solution[1])
## 2. When you have same lower bound and upper bound for each parameters, then you can use:
## + int or float: then you need to specify your problem size (number of dimensions)
problem2 = {
"obj_func": F5,
"lb": -10,
"ub": 30,
"minmax": "min",
"verbose": True,
"problem_size": 30, # Remember the keyword "problem_size"
}
md2 = BaseGA(problem2, epoch=10,
pop_size=50) # Remember the keyword "problem_size"
#!/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/thieu1995 % #-------------------------------------------------------------------------------------------------------% from opfunu.cec_basic.cec2014_nobias import * from mealpy.evolutionary_based.GA import BaseGA ## Setting parameters obj_func = F1 # lb = [-15, -10, -3, -15, -10, -3, -15, -10, -3, -15, -10, -3, -15, -10, -3] # ub = [15, 10, 3, 15, 10, 3, 15, 10, 3, 15, 10, 3, 15, 10, 3] lb = [-100] ub = [100] problem_size = 100 batch_size = 25 verbose = True epoch = 1000 pop_size = 50 md1 = BaseGA(obj_func, lb, ub, problem_size, batch_size, verbose, epoch, pop_size, 0.85, 0.05) best_pos1, best_fit1, list_loss1 = md1.train() print(md1.solution[0]) print(md1.solution[1]) print(md1.loss_train)
#!/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)