def f(chromossome):
route = Chromossome.get_fenotype(chromossome.get_genes())
total_weight = 0
total_vertexes = len(GRAPH)
for i in range(total_vertexes):
vertex = route[i]
next_vertex = route[i + 1]
edge_weight = GRAPH[vertex][next_vertex]
if edge_weight is None:
edge_weight = NON_ADJACENT_WEIGHT
total_weight += edge_weight
return total_weight
def f_chromossome(chromossome):
x, y = Chromossome.get_fenotype(chromossome.get_genes())
return f(x, y)
operators.mutation(population)
operators.elitism(population)
generation += 1
population_score = problem.g_average(population)
generation_plot.append(generation)
population_score_plot.append(population_score)
print(
f"Generation # {generation} -> Average population score = {population_score:.3f}\n"
)
best_chromossome = utils.find_best_chromossome(population)
print(f"Best individual: {utils.format_chromossome(best_chromossome)}")
board = Chromossome.get_fenotype(best_chromossome.get_genes())
for row in range(problem.N_QUEENS):
row_str = ""
for column in range(problem.N_QUEENS):
row_str += str(int(board[row][column])) + " "
print(row_str)
plt.gca().set_xlabel("Generation")
plt.gca().set_ylabel("Average fitness")
plt.gca().set_title("Average fitness per generation")
plt.plot(generation_plot, population_score_plot)
plt.show()