version with value

Author: bnafack

generic_algorithm_2.py

@@ -0,0 +1,99 @@
+import pandas as pd 
+import numpy as np
+import pygad
+import math
+import warnings
+warnings.filterwarnings("ignore")
+
+df = pd.read_excel('products.xlsx')
+obj= np.array(df['Product'])
+space = np.array(df['Space'])
+quantity= np.array(df['Quantity'])
+
+gene_space=[range(i) for i in quantity ]
+
+function_inputs = space # Function space.
+desired_output = 4 # volume capacity we want to reach.
+sol_per_population = int(math.pow(2,11)) # 2^11 number of sample in the initial population
+
+
+def fitness_func(solution, solution_idx):
+    """This function calculates the total fitness value by summing the product of 
+    each input and its corresponding space.
+    """
+    output = np.sum(solution*function_inputs)
+    if (output>desired_output): # discard a sample if the sum is more than the space available
+        fitness=0.0
+    else:
+        # The value 0.000001 is used to avoid the Inf value when the denominator numpy.abs(output - desired_output) is 0.0.
+        fitness = 1.0 / (np.abs(output - desired_output) + 0.000001)
+    return fitness
+
+def print_selected_obj(ga_instance,obj,space):
+
+    """This function generates all van loading combinations 
+    considering capacity, weight and size. It returns a list of possible combinations.
+    """
+    solution,_,_ = ga_instance.best_solution()
+    solution = np.array(solution).astype(float)
+    print('\n')
+    print(f'The optimal sum is {np.sum(solution*space)}')
+    print('\n')
+    print(f'The maximun price is {np.sum(solution*quantity)}')
+    print('\n')
+    print(f'object selected {obj[np.where(solution != 0)]}')
+    print('\n')
+
+
+
+def save_model(ga_instance):
+    filename = 'genetic'
+    ga_instance.save(filename=filename)
+
+
+def plot_fitnes(ga_instance):
+    ga_instance.plot_fitness(title="PyGAD with Adaptive Mutation", linewidth=5)
+
+
+def show_best_genes(ga_instance):
+    """The function will identify the genes (inputs) with 
+    the highest fitness value and display them as the best options.
+    """
+    solution, solution_fitness, solution_idx = ga_instance.best_solution()
+
+    print('\n')
+    print("Parameters of the best solution : {solution}".format(solution=solution))
+    print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
+
+    print('\n')
+
+
+# Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
+ga_instance = pygad.GA(num_generations=100,
+                       fitness_func=fitness_func,
+                       num_parents_mating=2,
+                       sol_per_pop=sol_per_population ,
+                       num_genes=len(function_inputs),
+                       mutation_type="adaptive",
+                       mutation_num_genes=(3, 1),
+                       gene_type=int,
+                       parallel_processing=4,
+                       gene_space = gene_space,
+                       keep_elitism=2,
+                       stop_criteria=["saturate_7"])
+
+
+
+
+if __name__=='__main__':
+    # Running the GA to optimize the parameters of the function.
+    ga_instance.run()
+
+    print_selected_obj(ga_instance,obj,space)
+
+    show_best_genes(ga_instance)
+    #Let's save the model.  
+
+    save_model(ga_instance)
+    plot_fitnes(ga_instance)
+