def execute(self, solution: BinarySolution) -> BinarySolution:
offspring = solution.variables
popsize = len(solution.variables[0])
values = [
np.random.choice(solution.variables[1][i]) for i in range(popsize)
]
probs = np.random.uniform(0, 1, popsize)
for i in range(popsize):
if probs[i] <= self.probability:
offspring[0][i] = values[i]
solution.variables = offspring
return solution
def create_solution(self) -> BinarySolution:
new_solution = BinarySolution(self.number_of_variables, self.number_of_objectives, self.number_of_constraints)
new_solution.variables = \
[self.sf.get_random_individual() for i in range(self.number_of_variables)]
return new_solution
