Files
EasyGA/src/EasyGA.py
2020-10-15 12:56:59 -04:00

84 lines
3.0 KiB
Python

import random
from attributes import attributes
class GA(attributes):
# Inhert all the ga attributes from the attributes class.
def __init__(self):
super(GA, self).__init__()
def evolve_generation(self, number_of_generations = 1, consider_termination = True):
"""Evolves the ga the specified number of generations."""
while(number_of_generations > 0 # Evolve the specified number of generations
and (not consider_termination # and if consider_termination flag is set
or self.termination_impl(self))): # then also check if termination conditions reached
# If its the first generation then initialize the population
if self.current_generation == 0:
self.initialize_population()
self.set_all_fitness()
self.population.sort_by_best_fitness(self)
# Otherwise evolve the population
else:
self.set_all_fitness()
self.population.sort_by_best_fitness(self)
self.parent_selection_impl(self)
self.crossover_population_impl(self)
self.survivor_selection_impl(self)
self.mutation_population_impl(self)
self.population.update()
number_of_generations -= 1
self.current_generation += 1
def evolve(self):
"""Runs the ga until the termination point has been satisfied."""
while(self.active()):
self.evolve_generation()
def active(self):
"""Returns if the ga should terminate based on the termination implimented."""
return self.termination_impl(self)
def initialize_population(self):
"""Initialize the population using
the initialization implimentation
that is currently set.
"""
self.population = self.initialization_impl(self)
def set_all_fitness(self):
"""Will get and set the fitness of each chromosome in the population.
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.
"""
# Check each chromosome
for chromosome in self.population.get_chromosome_list():
# Update fitness if needed or asked by the user
if(chromosome.get_fitness() is None or self.update_fitness):
chromosome.set_fitness(self.fitness_function_impl(chromosome))
def sort_by_best_fitness(self, chromosome_set):
"""Sorts the array by fitness.
1st element has highest fitness.
2nd element has second highest fitness.
etc.
"""
return sorted(chromosome_set, # list to be sorted
key = lambda chromosome: chromosome.get_fitness(), # by fitness
reverse = True) # from highest to lowest fitness