Update example.py

ahmedfgad · web-flow · commit 3820b0db0648 · 2020-05-13T19:24:39.000+02:00
diff --git a/example.py b/example.py
@@ -1,25 +1,6 @@
 import pygad
 import numpy
 
-num_generations = 50 # Number of generations.
-sol_per_pop = 8 # Number of solutions in the population.
-num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool.
-
-# Parameters of the mutation operation.
-mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists.
-mutation_num_genes = None # Number of genes to mutate. If the parameter mutation_num_genes exists, then no need for the parameter mutation_percent_genes.
-
-parent_selection_type = "tournament" # Type of parent selection.
-
-crossover_type = "two_points" # Type of the crossover operator.
-
-mutation_type = "scramble" # Type of the mutation operator.
-
-keep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.
-
-init_range_low = -2
-init_range_high = 5
-
 """
 Given the following function:
     y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6
@@ -30,30 +11,52 @@
 function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
 desired_output = 44 # Function output.
 
-num_genes = len(function_inputs)
-
-def fitness_func(solution):
+def fitness_func(solution, solution_idx):
     # 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.
     output = numpy.sum(solution*function_inputs)
     fitness = 1.0 / numpy.abs(output - desired_output)
     return fitness
 
+fitness_function = fitness_func
+
+num_generations = 50 # Number of generations.
+num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool.
+
+sol_per_pop = 8 # Number of solutions in the population.
+num_genes = len(function_inputs)
+
+init_range_low = -2
+init_range_high = 5
+
+
+parent_selection_type = "sss" # Type of parent selection.
+keep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.
+
+crossover_type = "single_point" # Type of the crossover operator.
+
+# Parameters of the mutation operation.
+mutation_type = "random" # Type of the mutation operator.
+mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists.
+
+def callback_generation(ga_instance):
+    print("Generation :", ga_instance.generations_completed)
+    print("Fitness of the best solution :", ga_instance.best_solution()[1])
+
 # Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
-ga_instance = pygad.GA(num_generations=num_generations, 
-          sol_per_pop=sol_per_pop, 
-          num_parents_mating=num_parents_mating, 
-          num_genes=num_genes,
-          fitness_func=fitness_func,
-          mutation_percent_genes=mutation_percent_genes,
-          mutation_num_genes=mutation_num_genes,
-          init_range_low=init_range_low,
-          init_range_high=init_range_high,
-          parent_selection_type=parent_selection_type,
-          crossover_type=crossover_type,
-          mutation_type=mutation_type,
-          keep_parents=keep_parents,
-          K_tournament=3)
+ga_instance = pygad.GA(num_generations=num_generations,
+                       num_parents_mating=num_parents_mating, 
+                       fitness_func=fitness_function,
+                       sol_per_pop=sol_per_pop, 
+                       num_genes=num_genes,
+                       init_range_low=init_range_low,
+                       init_range_high=init_range_high,
+                       parent_selection_type=parent_selection_type,
+                       keep_parents=keep_parents,
+                       crossover_type=crossover_type,
+                       mutation_type=mutation_type,
+                       mutation_percent_genes=mutation_percent_genes,
+                       callback_generation=callback_generation)
 
 # Running the GA to optimize the parameters of the function.
 ga_instance.run()
