def find_tsp_solution(self, fitness_function, dist_function, pop_size, gen,
m_rate, elite):
min_value = 1
max_value = self.repository.num_nodes
dim = self.repository.num_nodes + 1
ga = GA(fitness_function, min_value, max_value, dim, pop_size, elite,
m_rate)
ga.initialisation()
ga.evaluation()
overall_best = ga.best_chromosome()
progress = []
for g in range(gen):
best_chromosome = ga.best_chromosome()
progress.append(dist_function(best_chromosome.repres))
ga.next_generation_pool()
best_chromosome = ga.best_chromosome()
if best_chromosome.fitness > overall_best.fitness:
overall_best = best_chromosome
print('Best solution in generation ' + str(g) + ' is: x = ' +
str(best_chromosome.repres) + ' f(x) = ' +
str(best_chromosome.fitness))
print[i + 1 for i in overall_best.repres]
print dist_function(overall_best.repres)
self.repository.write_network("data/graph_out.txt",
overall_best.repres,
dist_function(overall_best.repres))
plt.plot(progress)
plt.ylabel('Distance')
plt.xlabel('Generation')
plt.show()
