paulin/EasyGA
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Alot of name changes and file name changes

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danielwilczak101
2020-10-04 23:56:51 -04:00
parent 80ebe8ca0c
commit 68db360b94
29 changed files with 166 additions and 161 deletions
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119
src/EasyGA.py
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@ -1,15 +1,17 @@
import random
# Import all the data structure prebuilt modules
from initialization import population as create_population
from initialization import chromosome as create_chromosome
from initialization import gene as create_gene
# Import example classes
from fitness_function import fitness_examples
from initialization import initialization_examples
from termination_point import termination_examples
from selection import selection_examples
from crossover import crossover_examples
from mutation import mutation_examples
from initialization import Population as create_population
from initialization import Chromosome as create_chromosome
from initialization import Gene as create_gene
# Structure Methods
from fitness_function import Fitness_methods
from initialization import Initialization_methods
from termination_point import Termination_methods
# Population Methods
from survivor_selection import Survivor_methods
# Manipulation Methods
from parent_selection import Parent_methods
from mutation import Mutation_methods
class GA:
def __init__(self):
@ -22,8 +24,9 @@ class GA:
self.population = None
# Termination varibles
self.current_generation = 0
self.current_fitness = 0
self.generation_goal = 3
self.current_fitness = 0
self.fitness_goal = 3
# Mutation variables
self.mutation_rate = 0.03
@ -32,21 +35,56 @@ class GA:
self.update_fitness = False
# Defualt EastGA implimentation structure
self.initialization_impl = initialization_examples.random_initialization
self.fitness_funciton_impl = fitness_examples.is_it_5
#self.mutation_impl = PerGeneMutation(Mutation_rate)
#self.selection_impl = TournamentSelection()
#self.crossover_impl = FastSinglePointCrossover()
self.termination_impl = termination_examples.generation_based
self.initialization_impl = Initialization_methods.random_initialization
self.fitness_funciton_impl = Fitness_methods.is_it_5
# Selects which chromosomes should be automaticly moved to the next population
#self.survivor_selection_impl = Survivor_methods.
# Methods for accomplishing parent-selection -> Crossover -> Mutation
#self.parent_selection_impl = Parent_methods.
#self.crossover_impl = Crossover_methods.
#self.mutation_impl = Mutation_methods.
# The type of termination to impliment
self.termination_impl = Termination_methods.generation_based
def evolve_generation(self, number_of_generations = 1):
"""Evolves the ga the specified number of generations."""
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.chromosome_list)
# Selection - Triggers flags in the chromosome if its been selected
# self.selection_impl(self)
# Crossover - Takes the flagged chromosome_list and crosses there genetic
# makup to make new offsprings.
# self.crossover_impl(self)
# Repopulate - Manipulates the population to some desired way
# self.repopulate_impl(self)
# Mutation - Manipulates the population very slightly
# self.mutation_impl(self)
# 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 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 active(self):
"""Returns if the ga should terminate base on the termination implimented"""
# Send termination_impl the whole ga class
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.population_size,
self.chromosome_length,
self.chromosome_impl,
self.gene_impl)
self.population = self.initialization_impl(self)
def get_population_fitness(self,population):
"""Will get and set the fitness of each chromosome in the population.
@ -61,43 +99,6 @@ class GA:
# Set the chromosomes fitness using the fitness function
chromosome.fitness = self.fitness_funciton_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 active(self):
"""Returns if the ga should terminate base on the termination implimented"""
# Send termination_impl the whole ga class
return self.termination_impl(self)
def evolve_generation(self, number_of_generations = 1):
"""Evolves the ga the specified number of generations."""
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)
# Selection - Triggers flags in the chromosome if its been selected
# self.selection_impl(self)
# Crossover - Takes the flagged chromosomes and crosses there genetic
# makup to make new offsprings.
# self.crossover_impl(self)
# Repopulate - Manipulates the population to some desired way
# self.repopulate_impl(self)
# Mutation - Manipulates the population very slightly
# self.mutation_impl(self)
# 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."""
return create_gene(value)