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Roulette Selection added

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RyleyGG
2020-10-06 18:46:34 -04:00
parent 49c98ba27c
commit 2f78c9f464
2 changed files with 36 additions and 16 deletions
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13
src/EasyGA.py
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@ -23,28 +23,29 @@ class GA:
self.gene_impl = None
self.population = None
self.target_fitness_type = 'maximum'
self.update_fitness = True
# Selection variables
self.parent_ratio = 0.1
# Termination varibles
# Termination variables
self.current_generation = 0
self.generation_goal = 50
self.current_fitness = 0
self.generation_goal = 250
self.fitness_goal = 9
# Mutation variables
self.mutation_rate = 0.10
# Rerun already computed fitness
self.update_fitness = True
# Defualt EastGA implimentation structure
# Default EasyGA implimentation structure
self.initialization_impl = Initialization_Methods().random_initialization
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
self.parent_selection_impl = Selection_Methods().Parent_Selection().Tournament().with_replacement
self.parent_selection_impl = Selection_Methods().Parent_Selection().Roulette().roulette_selection
self.crossover_impl = Crossover_Methods().single_point_crossover
self.mutation_impl = Mutation_Methods().per_gene_mutation
# The type of termination to impliment

37
src/selection/selection_methods.py
View File

@ -35,6 +35,34 @@ class Selection_Methods:
if random.uniform(0, 1) < selection_probability * pow(1-selection_probability, index+1):
ga.population.mating_pool.append(tournament_group[index])
class Roulette:
def roulette_selection(self, ga):
"""Roulette selection works based off of how strong the fitness is of the
chromosomes in the population. The stronger the fitness the higher the probability
that it will be selected. Using the example of a casino roulette wheel.
Where the chromosomes are the numbers to be selected and the board size for
those numbers are directly proportional to the chromosome's current fitness. Where
the ball falls is a randomly generated number between 0 and 1"""
total_fitness = sum(ga.population.chromosome_list[i].get_fitness() for i in range(len(ga.population.chromosome_list)))
rel_fitnesses = []
for chromosome in ga.population.chromosome_list:
if (total_fitness != 0):
rel_fitnesses.append(float(chromosome.fitness)/total_fitness)
probability = [sum(rel_fitnesses[:i+1]) for i in range(len(rel_fitnesses))]
while (len(ga.population.mating_pool) < len(ga.population.get_all_chromosomes())*ga.parent_ratio):
rand_number = random.random()
# Loop through the list of probabilities
for i in range(len(probability)):
# If the probability is greater than the random_number, then select that chromosome
if (probability[i] >= rand_number):
ga.population.mating_pool.append(ga.population.chromosome_list[i])
# print (f'Selected chromosome : {i}')
break
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:
@ -65,12 +93,3 @@ class Selection_Methods:
next_population.add_chromosome(ga.population.get_all_chromosomes()[iterator])
iterator += 1
return next_population
def roulette_selection(self, ga):
"""Roulette selection works based off of how strong the fitness is of the
chromosomes in the population. The stronger the fitness the higher the probability
that it will be selected. Using the example of a casino roulette wheel.
Where the chromosomes are the numbers to be selected and the board size for
those numbers are directly proportional to the chromosome's current fitness. Where
the ball falls is a randomly generated number between 0 and 1"""
pass