"best fit: %.4f" % (np.log(fitness_vector[best_index])),
"index: %d" % best_index,
"MSE: %.4f" % GenAlg.SquareLoss(best_fitness)])
print(informLine_temp)
informLines.append(informLine_temp)
saveLog(log_name, GA, informLines, date_round) # Save iteration information after every iteration
if (GA.criteria(best_fitness) == "converge"):
print("The algorithm meets its criteria, therefore stopped. ")
informLine_best = "Best fitness value: %.4f" % global_best_fitness
print(informLine_best)
informLines += ["The algorithm meets its criteria, therefore stopped. "]
informLines += [informLine_best]
initial_bezier_airfoil = GenAlg.BezierFitForOne(initial_best_airfoil)
best_bezier_airfoil = GenAlg.BezierFitForOne(global_best_airfoil)
plotList = [initial_bezier_airfoil, best_bezier_airfoil]
GenAlg.plotBestFig(plotList, int((gen+1)/10))
for i in range(len(pop_new)): optim_dict[str(i+1)] = pop_new[i]
break
# Save reesults every 10 eopchs
if ((gen+1) % 10 == 0):
informLine_best = "Best fitness value: %.4f" % global_best_fitness
print(informLine_best)
informLines += [informLine_best]
initial_bezier_airfoil = GenAlg.BezierFitForOne(initial_best_airfoil)
best_bezier_airfoil = GenAlg.BezierFitForOne(global_best_airfoil)
# print(global_best_airfoil[0:4,:])
plotList = [initial_bezier_airfoil, best_bezier_airfoil]
