Added save_data attribute to be able to turn off saving the population to the database.

EasyGA/EasyGA.py

@@ -84,6 +84,7 @@ class GA(Attributes):
             self.sort_by_best_fitness()
 
             # Save the population to the database
+            if self.save_data == True:
                 self.save_population()
 
             # Adapt the ga if the generation times the adapt rate

EasyGA/attributes.py

@@ -99,6 +99,7 @@ class Attributes:
             population = None,
             target_fitness_type = 'max',
             update_fitness = False,
+            save_data = True,
 
             parent_ratio = 0.10,
             selection_probability = 0.50,
@@ -151,6 +152,7 @@ class Attributes:
         self.population = population
         self.target_fitness_type = target_fitness_type
         self.update_fitness = update_fitness
+        self.save_data = save_data
 
         # Selection variables
         self.parent_ratio = parent_ratio

EasyGA/run.py

@@ -1,18 +1,33 @@
-import matplotlib.pyplot as plt
-
 import EasyGA
+import random
 
-for _ in range(2):
-    # Create a new genetic algorithm each.
-    ga = EasyGA.GA()
-    ga.evolve()
-    ga.print_population()
+# Create the Genetic algorithm
+ga = EasyGA.GA()
 
-# Graph the average of the two runs
-plt.subplot(1, 2, 1)
-ga.graph.highest_value_chromosome("average")
+ga.save_data = False
 
-plt.subplot(1, 2, 1)
-ga.graph.highest_value_chromosome("all")
+def is_it_5(chromosome):
+        """A very simple case test function - If the chromosomes gene value is a 5 add one
+         to the chromosomes overall fitness value."""
+        # Overall fitness value
+        fitness = 0
+        # For each gene in the chromosome
+        for gene in chromosome.gene_list:
+            # Check if its value = 5
+            if(gene.value == 5):
+                # If its value is 5 then add one to
+                # the overal fitness of the chromosome.
+                fitness += 1
 
-ga.graph.show()
+        return fitness
+
+ga.fitness_function_impl = is_it_5
+
+# Create random genes from 0 to 10
+ga.gene_impl = lambda: random.randint(0, 10)
+
+ga.evolve()
+
+# Print your default genetic algorithm
+ga.print_generation()
+ga.print_population()