Added the termination features

src/EasyGA.py

@@ -4,75 +4,91 @@ import random
 from initialization import population as create_population
 from initialization import chromosome as create_chromosome
 from initialization import gene as create_gene
-from fitness_function import default_fitness_example
+# Import the default fitness function
+from fitness_function import is_the_value_5
+# Import default termination points
+from termination_point import generation_based
+from termination_point import fitness_based
 # Import functionality defaults
 from initialization import random_initialization
 
 class GA:
     def __init__(self):
         """Initialize the GA."""
-        # Default variables
+        # Initilization variables
+        self.chromosome_length = 3
+        self.population_size = 5
         self.chromosome_impl = None
         self.gene_impl = None
         self.population = None
+        # Termination varibles
         self.current_generation = 0
-        self.generations = 3
-        self.chromosome_length = 3
-        self.population_size = 5
+        self.max_generations = 3
+        self.current_fitness = 0
+        self.goal_fitness = 3
+        # Mutation variables
         self.mutation_rate = 0.03
 
         # Defualt EastGA implimentation structure
         self.initialization_impl = random_initialization
-        self.fitness_funciton_impl = default_fitness_example
+        self.fitness_funciton_impl = is_the_value_5
         #self.mutation_impl = PerGeneMutation(Mutation_rate)
         #self.selection_impl = TournamentSelection()
         #self.crossover_impl = FastSinglePointCrossover()
-        #self.termination_impl = GenerationTermination(Total_generations)
+        self.termination_impl = generation_based
         #self.evaluation_impl = TestEvaluation()
 
         # If we want the fitnesses to be updated by the computer
         self.update_fitness = True
 
     def initialize_population(self):
-        """Initialize the population"""
+        """Initialize the population using the initialization
+         implimentation that is currently set"""
         self.population = self.initialization_impl(
         self.population_size,
         self.chromosome_length,
         self.chromosome_impl,
         self.gene_impl)
 
+    def get_population_fitness(self,population):
+        """Will get and set the fitness of each chromosome in the population"""
+        # Get each chromosome in the population
+        for chromosome in population:
+            # Set the chromosomes fitness using the fitness function
+            chromosome.fitness = self.fitness_funciton_impl(chromosome)
+
     def evolve(self):
         """Runs the ga until the termination point has been satisfied."""
-        self.initialize_population()
-        #while(self.active()):
-            #if(self.current_generation == 0):
-                #initialize_population()
+        # While the termination point hasnt been reached keep running
+        while(self.active()):
+            # If its the first generation then initialize the population
+            if(self.current_generation == 0):
+                # Initialize the population
+                self.initialize_population()
+            # First get the fitness of the population
+            self.get_population_fitness(self.population.chromosomes)
+
+            print(f"Ive completed generation - {self.current_generation}")
+
+            self.current_generation += 1
 
-            #get_fitness(population)
 
-        # run one iteration while the ga is active
-        #while self.active():
-            #self.evolve_generation(1)
 
     def active(self):
         """Returns if the ga should terminate base on the termination implimented"""
+        # Send termination_impl the whole ga class
         return self.termination_impl(self)
 
 
     def evolve_generation(self, number_of_generations):
         """Evolves the ga the specified number of generations.
         If update_fitness is set then all fitness values are updated.
-        Otherwise only fitness values set to None (i.e. uninitialized fitness values) are updated."""
+        Otherwise only fitness values set to None (i.e. uninitialized
+        fitness values) are updated."""
 
         # run the specified number of times
-        for n in range(number_of_generations):
+        #for n in range(number_of_generations):
 
-            # for each chromosome in the population
-            for chromosome in self.population.get_all_chromosomes():
-
-                # if the fitness should be updated, update it
-                if self.update_fitness or chromosome.get_fitness() is None:
-                    chromosome.set_fitness(self.fitness_impl(chromosome))
 
             # apply selection, crossover, and mutation
 

src/crossover/__init__.py

@@ -0,0 +1 @@
+# From file name import function name

src/fitness_function/__init__.py

@@ -1 +1,2 @@
-from .default_fitness_example import default_fitness_example
+# FROM (. means local) file_name IMPORT function_name
+from .is_the_value_5 import is_the_value_5

src/fitness_function/default_fitness_example.py

@@ -1,2 +0,0 @@
-def default_fitness_example():
-    pass

src/fitness_function/is_the_value_5.py

@@ -0,0 +1,14 @@
+def is_the_value_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.genes:
+        # 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
+    return fitness

src/initialization/__init__.py

@@ -1,4 +1,4 @@
-# __init__.py
+# FROM (. means local) file_name IMPORT function_name
 from .random_initialization import random_initialization
 from .population_structure.population import population
 from .chromosome_structure.chromosome import chromosome

src/run_testing.py

@@ -4,11 +4,10 @@ import random
 ga = EasyGA.GA()
 
 ga.chromosome_length = 3
-
+ga.max_generations = 5
 # If the user wants to use a domain
 ga.gene_impl = [random.randrange,1,10]
 
-
 # Run Everyhting
 ga.evolve()
 

src/termination_point/__init__.py

@@ -0,0 +1,3 @@
+# FROM (. means local) file_name IMPORT function_name
+from .generation_based import generation_based
+from .fitness_based import fitness_based

src/termination_point/fitness_based.py

@@ -0,0 +1,5 @@
+def fitness_based(ga):
+    status = True
+    if(ga.current_fitness > ga.goal_fitness):
+        status = False
+    return status

src/termination_point/generation_based.py

@@ -0,0 +1,5 @@
+def generation_based(ga):
+    status = True
+    if(ga.current_generation > ga.max_generations):
+        status = False
+    return status