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danielwilczak101
2020-09-30 19:33:23 -04:00
parent 625143da7d
commit 8377650c58
6 changed files with 31 additions and 22 deletions
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39
src/EasyGA.py
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@ -5,7 +5,7 @@ from initialization import population as create_population
from initialization import chromosome as create_chromosome
from initialization import gene as create_gene
# Import the default fitness function
from fitness_function import is_the_value_5
from fitness_function import example_is_it_5
# Import default termination points
from termination_point import generation_based
from termination_point import fitness_based
@ -29,14 +29,14 @@ class GA:
# Mutation variables
self.mutation_rate = 0.03
# Defualt EastGA implimentation structure
self.initialization_impl = random_initialization
self.fitness_funciton_impl = is_the_value_5
self.fitness_funciton_impl = example_is_it_5
#self.mutation_impl = PerGeneMutation(Mutation_rate)
#self.selection_impl = TournamentSelection()
#self.crossover_impl = FastSinglePointCrossover()
self.termination_impl = generation_based
#self.evaluation_impl = TestEvaluation()
# If we want the fitnesses to be updated by the computer
self.update_fitness = True
@ -61,17 +61,7 @@ class GA:
"""Runs the ga until the termination point has been satisfied."""
# 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
self.evolve_generation()
def active(self):
@ -80,17 +70,30 @@ class GA:
return self.termination_impl(self)
def evolve_generation(self, number_of_generations):
def evolve_generation(self, number_of_generations = 1):
"""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."""
while(number_of_generations > 0):
# 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)
# run the specified number of times
#for n in range(number_of_generations):
#selecion -> crossover -> mutation
#self.selection_impl(self)
#self.crossover_impl(self)
#self.mutation_impl(self)
#next_population.append(mutated_offspring)
# apply selection, crossover, and mutation
# Counter for the local number of generations in evolve_generation
number_of_generations -= 1
# Add one to the current overall generation
self.current_generation += 1
def make_gene(self,value):
"""Let's the user create a gene."""