plot the max and ave. scores together after the simulation.

increase the population size to 100

main.py

@@ -4,8 +4,9 @@ import src.genetic_algo as ga
 import src.simu_bike as sb
 import src.scorePlot as sp
 
-gen=ga.Genetic_algorithm(population_size=15,
-                         start_point=(0,5),
+gen=ga.Genetic_algorithm(population_size=100,
+                         start_point=(2,5),
                          side_length=3,
                          fit_func=sb.creat_bike)
-sp.plot(gen.run())
\ No newline at end of file
+x,y=gen.run()
+sp.plot(x,y)
\ No newline at end of file

src/genetic_algo.py

@@ -140,7 +140,10 @@ class Population():
         total = 0
         for i in self.population:
             total += i.fitness_score    
-        return total / self.__size   
+        return total / self.__size
+    def best_fitness_score(self):
+        return max([x.fitness_score for x in self.population])
+   
     def save_population_to_file(self,filename):
         with open(filename,"w") as f:
             json.dump([ind.coordinates for ind in self.population],f)
@@ -325,6 +328,7 @@ random.randint(start_point[1], start_point[1]+side_length)),
         last_ten_average = [0]*10
         file_counter=0
         score_list=[]
+        high_score_list=[]
         while abs(average_fitness_score- np.mean(last_ten_average[-10:])) > 0.1:
             last_ten_average.append(self.p.average_fitness_score())
             self.p.save_population_to_file("data/population_"+str(file_counter)+".json")
@@ -342,9 +346,10 @@ random.randint(start_point[1], start_point[1]+side_length)),
             # UNTIL population has converged
             # STOP
             score_list.append(average_fitness_score)
+            high_score_list.append(self.p.best_fitness_score())
             if len(score_list)>100:
                 break
-        return score_list
+        return score_list, high_score_list
         #return the fittest child among the last siblings
         # remaining_sibling = sorted(p.population, key=lambda x: x.fitness_score)
         # remaining_sibling.reverse()         

src/scorePlot.py

 import matplotlib.pyplot as plt
 
-def plot(scores):
+def plot(scores,high_score):
     plt.plot(range( len(scores)),scores,'--b')
+    plt.plot(range( len(high_score)),high_score,'-r')
     plt.xlabel('Generation')
     plt.ylabel('Ave. score')
     plt.show()