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demo_ga_udf.py
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# step1: define your own operator:
def selection_tournament(algorithm, tourn_size):
FitV = algorithm.FitV
sel_index = []
for i in range(algorithm.size_pop):
aspirants_index = np.random.choice(range(algorithm.size_pop), size=tourn_size)
sel_index.append(max(aspirants_index, key=lambda i: FitV[i]))
algorithm.Chrom = algorithm.Chrom[sel_index, :] # next generation
return algorithm.Chrom
# %% step2: import package and build ga, as usual.
import numpy as np
from sko.GA import GA, GA_TSP
demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2
ga = GA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, prob_mut=0.001,
lb=[-1, -10, -5], ub=[2, 10, 2], precision=[1e-7, 1e-7, 1])
# %% step3: register your own operator
ga.register(operator_name='selection', operator=selection_tournament, tourn_size=3)
# %% Or import the operators scikit-opt already defined.
from sko.operators import ranking, selection, crossover, mutation
ga.register(operator_name='ranking', operator=ranking.ranking). \
register(operator_name='crossover', operator=crossover.crossover_2point). \
register(operator_name='mutation', operator=mutation.mutation)
# %% Run ga
best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
# %% For advanced users
class MyGA(GA):
def selection(self, tourn_size=3):
FitV = self.FitV
sel_index = []
for i in range(self.size_pop):
aspirants_index = np.random.choice(range(self.size_pop), size=tourn_size)
sel_index.append(max(aspirants_index, key=lambda i: FitV[i]))
self.Chrom = self.Chrom[sel_index, :] # next generation
return self.Chrom
ranking = ranking.ranking
demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2
my_ga = MyGA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2],
precision=[1e-7, 1e-7, 1])
best_x, best_y = my_ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)