Added gene mutation rate

src/attributes.py

@@ -45,7 +45,8 @@ class attributes:
         self.fitness_goal       = None
 
         # Mutation variables
-        self.mutation_rate = 0.10
+        self.chromosome_mutation_rate = 0.10
+        self.gene_mutation_rate       = 0.01
 
         # Default EasyGA implimentation structure
         self.initialization_impl   = Initialization_Methods.random_initialization
@@ -86,5 +87,5 @@ class attributes:
     @population_size.setter
     def population_size(self, value_input):
         if(value_input == 0):
-            raise ValueError("Population size must be greater then 0")
+            raise ValueError("Population length must be greater then 0")
         self._population_size = value_input

src/mutation/mutation_methods.py

@@ -7,13 +7,18 @@ class Mutation_Methods:
 
         def random_selection(ga):
             """Selects random chromosomes"""
+            mutation_count = 0
 
-            # Loop through the population
-            for index in range(ga.population.size()):
+            # Loop until enough mutations occur
+            while mutation_count < ga.population.size()*ga.chromosome_mutation_rate:
 
-                # Randomly apply mutations
-                if random.uniform(0, 1) < ga.mutation_rate:
-                    ga.population.set_chromosome(ga.mutation_individual_impl(ga, ga.population.get_chromosome(index)), index)
+                # Loop through the population
+                for index in range(ga.population.size()):
+
+                    # Randomly apply mutations
+                    if random.uniform(0, 1) < ga.chromosome_mutation_rate:
+                        mutation_count += 1
+                        ga.population.set_chromosome(ga.mutation_individual_impl(ga, ga.population.get_chromosome(index)), index)
 
 
     class Individual:
@@ -43,19 +48,25 @@ class Mutation_Methods:
         def single_gene(ga, chromosome):
             """Mutates a random gene in the chromosome and resets the fitness."""
             chromosome.set_fitness(None)
+            mutation_count = 0
 
-            # Using the chromosome_impl
-            if ga.chromosome_impl != None:
-                index = random.randint(0, chromosome.size()-1)
-                chromosome.set_gene(ga.make_gene(ga.chromosome_impl()[index]), index)
+            # Loops until enough mutations occur
+            while mutation_count < chromosome.size()*ga.gene_mutation_rate:
+                mutation_count += 1
 
-            # Using the gene_impl
-            elif ga.gene_impl != None:
-                index = random.randint(0, chromosome.size()-1)
-                chromosome.set_gene(ga.make_gene(ga.gene_impl()), index)
+                # Using the chromosome_impl
+                if ga.chromosome_impl != None:
+                    index = random.randint(0, chromosome.size()-1)
+                    chromosome.set_gene(ga.make_gene(ga.chromosome_impl()[index]), index)
 
-            # Exit because no gene creation method specified
-            else:
-                print("You did not specify any initialization constraints.")
+                # Using the gene_impl
+                elif ga.gene_impl != None:
+                    index = random.randint(0, chromosome.size()-1)
+                    chromosome.set_gene(ga.make_gene(ga.gene_impl()), index)
+
+                # Exit because no gene creation method specified
+                else:
+                    print("You did not specify any initialization constraints.")
+                    break
 
             return chromosome

src/run_testing.py

@@ -4,13 +4,16 @@ import EasyGA
 # Create the Genetic algorithm
 ga = EasyGA.GA()
 
-# Mutate and reproduce frequently
-ga.parent_ratio  = 0.25
-ga.mutation_rate = 0.25
+# Reproduce 30% of the population.
+# Mutate 20% of the population.
+# Mutate 3% of the genes in each mutated chromosome.
+ga.parent_ratio  = 0.30
+ga.chromosome_mutation_rate = 0.20
+ga.gene_mutation_rate = 0.03
 
 # Create 25 chromosomes each with 10 genes
-ga.population_size = 50
-ga.chromosome_length = 10
+ga.population_size = 100
+ga.chromosome_length = 25
 
 # Create random genes from 0 to 10
 ga.gene_impl = lambda: random.randint(0, 10)
@@ -21,7 +24,7 @@ ga.target_fitness_type = 'min'
 
 # Terminate when a chromosome has all 0's
 ga.fitness_goal = 0
-ga.generation_goal = None
+ga.generation_goal = 150
 
 while ga.active():
     ga.evolve_generation(10)