GA END

- BUGfix
- comments
- stop var

with Michał Malinowski

AI/GeneticAlgorithm.py

@@ -39,10 +39,11 @@ def genetic_algorithm_setup(field):
 
     best_outputs = []
     num_generations = 10
-    num_parents = 2
+    num_parents = 4
 
     # iterative var
     generation = 0
+    stop = 0
     # TODO WARUNEK STOPU
     while generation < num_generations and stop < 3:
         if keyboard.is_pressed('space'):
@@ -55,7 +56,7 @@ def genetic_algorithm_setup(field):
             fitness = []
 
             for i in range(0, population_size):
-                print(len(population_text), i)
+                print('POP_TEXT', len(population_text), i)
                 fitness.append((i, population_fitness(population_text[i], field, population_size)))
 
             print("Fitness")
@@ -99,12 +100,20 @@ def genetic_algorithm_setup(field):
                                  for row in offspring_mutation[i]]))
                 print("")
 
+            population_text_copy = copy.deepcopy(population_text)
+            unused_indexes = [i for i in range(0, population_size) if i not in [j[0] for j in best]]
             # Creating next generation
             population_text = []
             for k in range(0, len(parents)):
-                population_text.append(parents)
+                population_text.append(parents[k])
             for k in range(0, len(offspring_mutation)):
                 population_text.append(offspring_mutation[k])
+            while len(population_text) < population_size:
+                x = random.choice(unused_indexes)
+                population_text.append(population_text_copy[x])
+                unused_indexes.remove(x)
+
+            print("END LEN", len(population_text))
 
             # TODO WARUNEK STOPU
             stop = 0

AI/ga_methods.py

@@ -2,6 +2,7 @@ import copy
 import random
 
 import matplotlib
+import matplotlib.pyplot
 import numpy
 
 import src.dimensions as D
@@ -28,7 +29,7 @@ def local_fitness(field, x, y, plants_case):
         plant_value = 1
 
     neighbour_bonus = 1
-    print(x, y)
+
     if x - 1 >= 0:
         if plants_case[x][y] == plants_case[x - 1][y]:
             neighbour_bonus += 1
@@ -47,11 +48,12 @@ def local_fitness(field, x, y, plants_case):
     return local_fitness_value
 
 
-def population_fitness(population_text, field, population_size):
+def population_fitness(population_text_local, field, population_size):
     # Calculating the fitness value of each solution in the current population.
     # The fitness function calulates the sum of products between each input and its corresponding weight.
     fitness = []
 
+    print('LOCAL', len(population_text_local))
     for k in range(population_size):
         population_values_single = []
         population_values_single_row = []
@@ -59,7 +61,7 @@ def population_fitness(population_text, field, population_size):
 
         for i in range(0, D.GSIZE):
             for j in range(0, D.GSIZE):
-                population_values_single_row.append(local_fitness(field, i, j, population_text))
+                population_values_single_row.append(local_fitness(field, i, j, population_text_local))
             population_values_single.append(population_values_single_row)
 
         for i in range(D.GSIZE):
@@ -73,7 +75,7 @@ def crossover(parents):
     for i in range(0, len(parents)):
         child = copy.deepcopy(parents[i])
         # Vertical randomization
-        width = random.randint(1, D.GSIZE / len(parents))  # width of stripes
+        width = random.randint(1, D.GSIZE // len(parents))  # width of stripes
         indexes_parents = numpy.random.permutation(range(0, len(parents)))  # sorting of stripes
         beginning = random.randint(0, len(parents[0]) - width * len(parents))  # point we start putting the stripes from
         for x in indexes_parents:
@@ -92,15 +94,6 @@ def mutation(population_units, offspring_crossover, num_mutants, num_mutations=1
             offspring_crossover[case][mutation_x][mutation_y] = mutation_value
             num_mutants -= 1
 
-    while num_mutants > 0:
-        case = random.randint(0, len(offspring_crossover))
-        for mutation in range(0, num_mutations):
-            mutation_x = random.randint(0, D.GSIZE - 1)
-            mutation_y = random.randint(0, D.GSIZE - 1)
-            mutation_value = random.choice(population_units)
-            offspring_crossover[case][mutation_x][mutation_y] = mutation_value
-            num_mutants -= 1
-
     return offspring_crossover