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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)