Removed unnecessary code and decorator naming

src/crossover/crossover_methods.py

@@ -32,39 +32,11 @@ def _check_weight(individual_method):
     return new_method
 
 
-def _genes_to_chromosome(individual_method):
-    """Converts a collection of genes into a chromosome.
-    Note: Will recreate the gene list if given gene list.
-          Built-in methods do not construct gene lists
-          and use yield for efficiency.
-    """
-
-    return lambda ga, parent_1, parent_2, weight:\
-        ga.make_chromosome(
-            individual_method(ga, parent_1, parent_2, weight)
-        )
-
-
-def _values_to_genes(individual_method):
-    """Converts a collection of values into genes.
-    Returns a generator of genes to avoid storing a new list.
-    """
-
-    return lambda ga, parent_1, parent_2, weight:\
-        (
-            ga.make_gene(value)
-            for value
-            in individual_method(ga, parent_1, parent_2, weight)
-        )
-
-
 class Crossover_Methods:
 
     # Allowing access to decorators when importing class
     _append_to_next_population = _append_to_next_population
     _check_weight              = _check_weight
-    _genes_to_chromosome       = _genes_to_chromosome
-    _values_to_genes           = _values_to_genes
 
 
     class Population:
@@ -107,7 +79,6 @@ class Crossover_Methods:
 
 
         @_check_weight
-        @_genes_to_chromosome
         def single_point(ga, parent_1, parent_2, weight = 0.5):
             """Cross two parents by swapping genes at one random point."""
 
@@ -134,14 +105,12 @@ class Crossover_Methods:
 
 
         @_check_weight
-        @_genes_to_chromosome
         def multi_point(ga, parent_1, parent_2, weight = 0.5):
             """Cross two parents by swapping genes at multiple points."""
             pass
 
 
         @_check_weight
-        @_genes_to_chromosome
         def uniform(ga, parent_1, parent_2, weight = 0.5):
             """Cross two parents by swapping all genes randomly."""
 
@@ -152,8 +121,6 @@ class Crossover_Methods:
         class Arithmetic:
             """Crossover methods for numerical genes."""
 
-            @_genes_to_chromosome
-            @_values_to_genes
             def average(ga, parent_1, parent_2, weight = 0.5):
                 """Cross two parents by taking the average of the genes."""
 
@@ -170,8 +137,6 @@ class Crossover_Methods:
                     yield value
 
 
-            @_genes_to_chromosome
-            @_values_to_genes
             def extrapolate(ga, parent_1, parent_2, weight = 0.5):
 
                 """Cross two parents by extrapolating towards the first parent.
@@ -192,8 +157,6 @@ class Crossover_Methods:
 
 
             @_check_weight
-            @_genes_to_chromosome
-            @_values_to_genes
             def random(ga, parent_1, parent_2, weight = 0.5):
                 """Cross two parents by taking a random integer or float value between each of the genes."""
 
@@ -219,11 +182,11 @@ class Crossover_Methods:
 
                     yield value
 
+
         class Permutation:
             """Crossover methods for permutation based chromosomes."""
 
             @_check_weight
-            @_genes_to_chromosome
             def ox1(ga, parent_1, parent_2, weight = 0.5):
                 """Cross two parents by slicing out a random part of one parent
                 and then filling in the rest of the genes from the second parent."""
@@ -269,11 +232,12 @@ class Crossover_Methods:
 
 
             @_check_weight
-            @_genes_to_chromosome
             def partially_mapped(ga, parent_1, parent_2, weight = 0.5):
                 """Cross two parents by slicing out a random part of one parent
                 and then filling in the rest of the genes from the second parent,
-                preserving the ordering of genes wherever possible."""
+                preserving the ordering of genes wherever possible.
+
+                NOTE: Needs to be fixed."""
 
                 # Too small to cross
                 if len(parent_1) < 2:

src/mutation/mutation_methods.py

@@ -15,6 +15,7 @@ def _check_chromosome_mutation_rate(population_method):
         else:
             raise ValueError("Chromosome mutation rate must be between 0 and 1.")
 
+    new_method.__name__ = population_method.__name__
     return new_method
 
 
@@ -26,12 +27,13 @@ def _check_gene_mutation_rate(individual_method):
         if not isinstance(ga.gene_mutation_rate, float):
             raise TypeError("Gene mutation rate must be a float.")
 
-        elif 0 < ga.gene_mutation_rate < 1:
+        elif 0 < ga.gene_mutation_rate <= 1:
             individual_method(ga, index)
 
         else:
             raise ValueError("Gene mutation rate must be between 0 and 1.")
 
+    new_method.__name__ = individual_method.__name__
     return new_method
 
 
@@ -42,24 +44,7 @@ def _reset_fitness(individual_method):
         chromosome.fitness = None
         individual_method(ga, chromosome)
 
-    return new_method
+    new_method.__name__ = individual_method.__name__
-
-
-def _loop_random_selections(population_method):
-    """Runs the population method until enough chromosomes are mutated.
-    Provides the indexes of selected chromosomes to mutate using
-    random.sample to get all indexes fast.
-    """
-
-    def new_method(ga):
-
-        sample_space = range(len(ga.population))
-        sample_size  = ceil(len(ga.population)*ga.chromosome_mutation_rate)
-
-        # Loop the population method until enough chromosomes are mutated.
-        for index in random.sample(sample_space, sample_size):
-            population_method(ga, index)
-
     return new_method
 
 
@@ -78,6 +63,7 @@ def _loop_random_mutations(individual_method):
         for index in random.sample(sample_space, sample_size):
             individual_method(ga, chromosome, index)
 
+    new_method.__name__ = individual_method.__name__
     return new_method
 
 
@@ -86,7 +72,6 @@ class Mutation_Methods:
     _check_chromosome_mutation_rate = _check_chromosome_mutation_rate
     _check_gene_mutation_rate       = _check_gene_mutation_rate
     _reset_fitness = _reset_fitness
-    _loop_random_selections = _loop_random_selections
     _loop_random_mutations  = _loop_random_mutations
 
 
@@ -94,10 +79,14 @@ class Mutation_Methods:
         """Methods for selecting chromosomes to mutate"""
 
         @_check_chromosome_mutation_rate
-        @_loop_random_selections
+        def random_selection(ga):
-        def random_selection(ga, index):
             """Selects random chromosomes."""
 
+            sample_space = range(len(ga.population))
+            sample_size  = ceil(len(ga.population)*ga.chromosome_mutation_rate)
+
+            # Loop the individual method until enough genes are mutated.
+            for index in random.sample(sample_space, sample_size):
                 ga.mutation_individual_impl(ga, ga.population[index])
 
 
@@ -112,6 +101,17 @@ class Mutation_Methods:
                 ga.mutation_individual_impl(ga, ga.population[index])
 
 
+        @_check_chromosome_mutation_rate
+        def best_replace_worst(ga):
+            """Selects the best chromosomes, copies them, and replaces the worst chromosomes."""
+
+            mutation_amount = ceil(ga.chromosome_mutation_rate*len(ga.population))
+
+            for i in range(mutation_amount):
+                ga.population[-i-1] = ga.make_chromosome(ga.population[i])
+                ga.mutation_individual_impl(ga, ga.population[-i-1])
+
+
     class Individual:
         """Methods for mutating a single chromosome."""
 

src/parent_selection/parent_selection_methods.py

@@ -12,6 +12,7 @@ def _check_selection_probability(selection_method):
         else:
             raise Exception("Selection probability must be between 0 and 1 to select parents.")
 
+    new_method.__name__ = selection_method.__name__
     return new_method
 
 
@@ -27,6 +28,7 @@ def _check_positive_fitness(selection_method):
         else:
             raise Exception("Converted fitness values can't have negative values or be all 0. Consider using rank selection or stochastic selection instead.")
 
+    new_method.__name__ = selection_method.__name__
     return new_method
 
 
@@ -39,6 +41,7 @@ def _ensure_sorted(selection_method):
         ga.population.sort_by_best_fitness(ga)
         selection_method(ga)
 
+    new_method.__name__ = selection_method.__name__
     return new_method
 
 
@@ -52,6 +55,7 @@ def _compute_parent_amount(selection_method):
         parent_amount = max(2, round(len(ga.population)*ga.parent_ratio))
         selection_method(ga, parent_amount)
 
+    new_method.__name__ = selection_method.__name__
     return new_method
 
 

src/survivor_selection/survivor_selection_methods.py

@@ -3,9 +3,12 @@ import random
 def _append_to_next_population(survivor_method):
     """Appends the selected chromosomes to the next population."""
 
-    return lambda ga:\
+    def new_method(ga):
         ga.population.append_children(survivor_method(ga))
 
+    new_method.__name__ = survivor_method.__name__
+    return new_method
+
 
 class Survivor_Selection:
     """Survivor selection determines which individuals should be brought to the next generation"""