Saving and loading the genetic algorithm

An instance of the genetic algorithm is saved using the pickle library and then loaded to resotre its state.

Author: ahmedfgad

example.py

@@ -7,33 +7,55 @@
 What are the best values for the 6 weights (w1 to w6)? We are going to use the genetic algorithm to optimize this function.
 """
 
-# Equation inputs.
 function_inputs = [4,-2,3.5,5,-11,-4.7]
-# Equation output.
-function_output = 44
+function_output = 44 # Function output.
 
+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.
-num_generations = 50 # Number of generations.
 
 # Parameters of the mutation operation.
-mutation_percent_genes=10 # Percentage of genes to mutate.
+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.
 
 # Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
 ga_instance = ga.GA(num_generations=num_generations, 
           sol_per_pop=sol_per_pop, 
           num_parents_mating=num_parents_mating, 
           function_inputs=function_inputs,
           function_output=function_output,
-          mutation_percent_genes=10)
+          mutation_percent_genes=mutation_percent_genes,
+          mutation_num_genes=mutation_num_genes,
+          parent_selection_type=parent_selection_type,
+          crossover_type=crossover_type,
+          mutation_type=mutation_type,
+          keep_parents=keep_parents,
+          K_tournament=3)
 
-# Training the GA to optimize the parameters of the function.
-ga_instance.train()
+# Running the GA to optimize the parameters of the function.
+ga_instance.run()
 
 # After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.
 ga_instance.plot_result()
 
 # Returning the details of the best solution.
 best_solution, best_solution_fitness = ga_instance.best_solution()
 print("Parameters of the best solution :", best_solution)
-print("Fitness value of the best solution :", best_solution_fitness)
+print("Fitness value of the best solution :", best_solution_fitness, "\n")
+
+# Saving the GA instance.
+filename = 'genetic' # The filename to which the instance is saved. The name is without extension.
+ga_instance.save(filename=filename)
+
+# Loading the saved GA instance.
+loaded_ga_instance = ga.load(filename=filename)
+loaded_ga_instance.plot_result()
+print(loaded_ga_instance.best_solution())