def get_solution(self): self.display_method = "Simulated annealing" self.display_parameters = { 'initial_temp': self.initial_temp, 'decrement': self.decrement } return TSPSolver.get_solution(self)
def get_solution(self): self.display_method = "Genetic algorithm" self.display_parameters = { 'cr': self._cr_, 'mr': self._mr_, 'chromosomes': self._chromosomes_, 'selection_method': self._params_['selection_method'], 'crossover_method': self._params_['crossover_method'], 'mutation_method': self._params_['mutation_method'] } return TSPSolver.get_solution(self)
def get_solution(self): self.display_method = "Genetic" self.display_parameters = { 'cr': self._cr, 'mr': self._mr, 'chromosomes': self._chromosomes, 'initial_population_method': self._g_args.get_method_initial_population(), 'selection_method': self._g_args.get_method_select(), 'crossover_method': self._g_args.get_method_cross(), 'mutation_method': self._g_args.get_method_mutate() } if (self._g_args.get_method_initial_population() == INITIAL_POPULATION_SEMIACS): self.display_parameters['initial_ants'] = \ self._g_args.get_initial_ants() return TSPSolver.get_solution(self)
def get_solution(self): self.display_method = "Brute force" self.display_parameters = {'solution_canceled': self.solution_canceled} return TSPSolver.get_solution(self)