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Optimizations/updates

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1. Deleted duplicate functions in EasyGA
2. Added new index-dependent fitness example
3. GA now auto-sorts by best fitness immediately after the fitness is calculated across the board
4. Removed 'selected' status flag from the Chromosome flag
5. Added mating_pool attribute to the population
6. Changed other code to be in line with 4 and 5
7. Optimized tournament selection method
This commit is contained in:
RyleyGG
2020-10-06 17:55:17 -04:00
parent 3bfa962194
commit e7ac0e23f4
7 changed files with 61 additions and 104 deletions
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53
src/EasyGA.py
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@ -18,17 +18,20 @@ class GA:
"""Initialize the GA."""
# Initilization variables
self.chromosome_length = 10
self.population_size = 100
self.population_size = 150
self.chromosome_impl = None
self.gene_impl = None
self.population = None
self.target_fitness_type = 'maximum'
self.parent_ratio = 0.1
# Termination varibles
self.current_generation = 0
self.generation_goal = 50
self.current_fitness = 0
self.generation_goal = 100
self.fitness_goal = 3
self.generation_goal = 250
self.fitness_goal = 9
# Mutation variables
self.mutation_rate = 0.10
@ -37,7 +40,7 @@ class GA:
# Defualt EastGA implimentation structure
self.initialization_impl = Initialization_Methods().random_initialization
self.fitness_function_impl = Fitness_Examples().is_it_5
self.fitness_function_impl = Fitness_Examples().index_dependent_values
# Selects which chromosomes should be automaticly moved to the next population
self.survivor_selection_impl = Selection_Methods().Survivor_Selection().remove_two_worst
# Methods for accomplishing parent-selection -> Crossover -> Mutation
@ -54,6 +57,7 @@ class GA:
if self.current_generation == 0:
self.initialize_population()
self.set_all_fitness(self.population.chromosome_list)
self.population.set_all_chromosomes(self.sort_by_best_fitness())
self.parent_selection_impl(self)
next_population = self.crossover_impl(self)
@ -62,6 +66,7 @@ class GA:
self.population = next_population
self.set_all_fitness(self.population.chromosome_list)
self.population.set_all_chromosomes(self.sort_by_best_fitness())
number_of_generations -= 1
self.current_generation += 1
@ -94,34 +99,26 @@ class GA:
# Set the chromosomes fitness using the fitness function
chromosome.set_fitness(self.fitness_function_impl(chromosome))
def evolve(self):
"""Runs the ga until the termination point has been satisfied."""
# While the termination point hasnt been reached keep running
while(self.active()):
self.evolve_generation()
def sort_by_best_fitness(self, chromosome_set = None):
def evolve_generation(self, number_of_generations = 1, consider_termination = True):
"""Evolves the ga the specified number of generations."""
while(number_of_generations > 0 and (consider_termination == False or self.termination_impl(self))):
# If its the first generation then initialize the population
if self.current_generation == 0:
self.initialize_population()
self.set_all_fitness(self.population.chromosome_list)
self.parent_selection_impl(self)
next_population = self.crossover_impl(self)
next_population = self.survivor_selection_impl(self, next_population)
next_population.set_all_chromosomes(self.mutation_impl(self, next_population.get_all_chromosomes()))
if chromosome_set == None:
chromosome_set = self.population.get_all_chromosomes()
self.population = next_population
self.set_all_fitness(self.population.chromosome_list)
chromosome_set_temp = chromosome_set
not_sorted_check = 0
while (not_sorted_check != len(chromosome_set_temp)):
not_sorted_check = 0
for i in range(len(chromosome_set_temp)):
if ((i + 1 < len(chromosome_set_temp)) and (chromosome_set_temp[i + 1].fitness > chromosome_set_temp[i].fitness)):
temp = chromosome_set[i]
chromosome_set_temp[i] = chromosome_set[i + 1]
chromosome_set_temp[i + 1] = temp
else:
not_sorted_check += 1
number_of_generations -= 1
self.current_generation += 1
chromosome_set = chromosome_set_temp
def active(self):
"""Returns if the ga should terminate base on the termination implimented"""
return self.termination_impl(self)
return chromosome_set
def make_gene(self,value):
return create_gene(value)

5
src/crossover/crossover_methods.py
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@ -14,10 +14,7 @@ class Crossover_Methods:
"""Single point crossover is when a "point" is selected and the genetic
make up of the two parent chromosomes are "Crossed" or better known as swapped"""
crossover_pool = []
for i in range(ga.population_size):
if ga.population.get_all_chromosomes()[i].selected:
crossover_pool.append(ga.population.get_all_chromosomes()[i])
crossover_pool = ga.population.mating_pool
new_population = Population()
for i in range(len(crossover_pool)):

12
src/fitness_function/fitness_examples.py
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@ -14,3 +14,15 @@ class Fitness_Examples:
fitness += 1
return fitness
def index_dependent_values(self, 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 i in range(len(chromosome.gene_list)):
if (chromosome.gene_list[i].value == i+1):
fitness += 1
return fitness

1
src/initialization/chromosome_structure/chromosome.py
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@ -6,7 +6,6 @@ class Chromosome:
else:
self.gene_list = genes
self.fitness = None
self.selected = False
def add_gene(self, gene, index = -1):
if index == -1:

1
src/initialization/population_structure/population.py
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@ -7,6 +7,7 @@ class Population:
else:
self.chromosome_list = chromosomes
self.fitness = None
self.mating_pool = []
def get_closet_fitness(self,value):
# Get the chomosome that has the closets fitness to the value defined

2
src/run_testing.py
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@ -13,4 +13,4 @@ ga.gene_impl = [random.randrange,1,100]
ga.evolve()
# Print the current population
ga.population.print_all()
ga.population.print_all()

91
src/selection/selection_methods.py
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@ -12,68 +12,33 @@ class Selection_Methods:
class Parent_Selection:
class Tournament:
def with_replacement(self, ga):
tournament_size = int(len(ga.population.get_all_chromosomes())/10) #currently hard-coded for purposes of the example.
tournament_size = int(len(ga.population.get_all_chromosomes())*ga.parent_ratio/10)
if tournament_size < 3:
tournament_size = int(len(ga.population.get_all_chromosomes())/3)
parent_ratio = 0.25
#selection_probability is the likelihood that a chromosome will be selected.
#best chromosome in a tournament is given a selection probablity of selection_probability
#2nd best is given probability of selection_probability*(1-selection_probability)
#3rd best is given probability of selection_probability*(1-selection_probability)**2
tournament_size = int(len(ga.population.get_all_chromosomes())*ga.parent_ratio/3)
# Probability used for determining if a chromosome should enter the mating pool.
selection_probability = 0.95
total_selected = 0 #Total Chromosomes selected
while (total_selected < parent_ratio*ga.population_size):
#create & gather tournament group
tournament_group = []
for i in range(tournament_size):
tournament_group.append(random.choice(ga.population.get_all_chromosomes()))
# Repeat tournaments until the mating pool is large enough.
while (len(ga.population.mating_pool) < len(ga.population.get_all_chromosomes())*ga.parent_ratio):
#Sort the tournament contenders based on their fitness
#currently hard-coded to only consider higher fitness = better; can be changed once this impl is agreed on
#also currently uses bubble sort because its easy
tournament_group_temp = tournament_group
not_sorted_check = 0
while (not_sorted_check != len(tournament_group_temp)):
not_sorted_check = 0
for i in range(len(tournament_group_temp)):
if ((i + 1 < len(tournament_group_temp)) and (tournament_group_temp[i + 1].fitness > tournament_group_temp[i].fitness)):
temp = tournament_group[i]
tournament_group_temp[i] = tournament_group[i + 1]
tournament_group_temp[i + 1] = temp
else:
not_sorted_check += 1
tournament_group = tournament_group_temp
#After sorting by fitness, randomly select a chromosome based on selection_probability
selected_chromosome_tournament_index = 0
for i in range(tournament_size):
random_num = random.uniform(0,1)
#ugly implementation but its functional
if i == 0:
if random_num <= selection_probability:
tournament_group[i].selected = True
total_selected += 1
selected_chromosome_tournament_index = i
break
else:
if random_num <= selection_probability*((1-selection_probability)**(i-1)):
tournament_group[i].selected = True
total_selected += 1
selected_chromosome_tournament_index = i
break
# Generate a random tournament group and sort by fitness.
tournament_group = ga.sort_by_best_fitness([random.choice(ga.population.get_all_chromosomes()) for n in range(tournament_size)])
# For each chromosome, add it to the mating pool based on its rank in the tournament.
for index in range(tournament_size):
# Probability required is selection_probability * (1-selection_probability) ^ (tournament_size-index+1)
# e.g. top ranked fitness has probability: selection_probability
# second ranked fitness has probability: selection_probability * (1-selection_probability)
# third ranked fitness has probability: selection_probability * (1-selection_probability)^2
# etc.
if random.uniform(0, 1) < selection_probability * pow(1-selection_probability, index+1):
ga.population.mating_pool.append(tournament_group[index])
class Survivor_Selection:
def repeated_crossover(self, ga, next_population): #Might be cheating? I don't know honestly - RG
while len(next_population.get_all_chromosomes()) < ga.population_size:
crossover_pool = []
for i in range(ga.population_size):
if ga.population.get_all_chromosomes()[i].selected:
crossover_pool.append(ga.population.get_all_chromosomes()[i])
crossover_pool = ga.population.mating_pool
split_point = random.randint(0,ga.chromosome_length)
chromosome_list = []
@ -95,23 +60,9 @@ class Selection_Methods:
return next_population
def remove_two_worst(self, ga, next_population):
#Bubble sorting by highest fitness
temp_population = ga.population
not_sorted_check = 0
while (not_sorted_check != len(temp_population.get_all_chromosomes())):
not_sorted_check = 0
for i in range(len(temp_population.get_all_chromosomes())):
if ((i + 1 < len(temp_population.get_all_chromosomes())) and (temp_population.get_all_chromosomes()[i + 1].fitness > temp_population.get_all_chromosomes()[i].fitness)):
temp = temp_population.get_all_chromosomes()[i]
temp_population.get_all_chromosomes()[i] = ga.population.get_all_chromosomes()[i + 1]
temp_population.get_all_chromosomes()[i + 1] = temp
else:
not_sorted_check += 1
iterator = 0
while len(next_population.get_all_chromosomes()) < ga.population_size:
next_population.add_chromosome(temp_population.get_all_chromosomes()[iterator])
next_population.add_chromosome(ga.population.get_all_chromosomes()[iterator])
iterator += 1
return next_population