paulin/EasyGA
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Minor edits

Browse Source
This commit is contained in:
SimpleArt
2020-10-25 22:36:54 -04:00
parent 3625453a28
commit ae74ce1766
3 changed files with 16 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
src/attributes.py
View File

@ -19,6 +19,8 @@ from mutation import Mutation_Methods
from crossover import Crossover_Methods
class attributes:
"""SAMPLE TEXT"""
def __init__(self):
"""Initialize the GA."""
@ -55,7 +57,7 @@ class attributes:
# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
self.parent_selection_impl = Parent_Selection.Rank.tournament
self.crossover_individual_impl = Crossover_Methods.Individual.single_point
self.crossover_population_impl = Crossover_Methods.Population.random_selection
self.crossover_population_impl = Crossover_Methods.Population.sequential_selection
self.survivor_selection_impl = Survivor_Selection.fill_in_best
self.mutation_individual_impl = Mutation_Methods.Individual.single_gene
self.mutation_population_impl = Mutation_Methods.Population.random_selection
@ -67,6 +69,7 @@ class attributes:
# Getter and setters for all varibles
@property
def chromosome_length(self):
"""SAMPLE TEXT"""
return self._chromosome_length
@chromosome_length.setter

20
src/crossover/crossover_methods.py
View File

@ -3,7 +3,7 @@ import random
class Crossover_Methods:
class Population:
"""Methods for selecting chromosomes to crossover"""
"""Methods for selecting chromosomes to crossover."""
def sequential_selection(ga):
"""Select sequential pairs from the mating pool.
@ -41,22 +41,22 @@ class Crossover_Methods:
class Individual:
"""Methods for crossing parents"""
"""Methods for crossing parents."""
def single_point(ga, parent_one, parent_two):
"""Cross two parents by swapping genes at one random point"""
"""Cross two parents by swapping genes at one random point."""
index = random.randint(0, parent_one.size()-1)
return ga.make_chromosome(parent_one.get_gene_list()[:index] + parent_two.get_gene_list()[index:])
def multi_point(ga, parent_one, parent_two):
"""Cross two parents by swapping genes at multiple points"""
"""Cross two parents by swapping genes at multiple points."""
pass
def uniform(ga, parent_one, parent_two):
"""Cross two parents by swapping all genes randomly"""
"""Cross two parents by swapping all genes randomly."""
return ga.make_chromosome([ # Make a new chromosome
random.choice([ # by selecting random genes from
@ -66,10 +66,10 @@ class Crossover_Methods:
for i in range(parent_one.size())]) # for each gene
class Arithmetic:
"""Crossover methods for numerical genes"""
"""Crossover methods for numerical genes."""
def int_random(ga, parent_one, parent_two):
"""Cross two parents by taking a random integer value between each of the genes"""
"""Cross two parents by taking a random integer value between each of the genes."""
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene( # filled with new genes
@ -81,7 +81,7 @@ class Crossover_Methods:
def int_weighted(ga, parent_one, parent_two):
"""Cross two parents by taking a a weighted average of the genes"""
"""Cross two parents by taking a a weighted average of the genes."""
# the percentage of genes taken from the first gene
weight = 0.25
@ -95,7 +95,7 @@ class Crossover_Methods:
def float_random(ga, parent_one, parent_two):
"""Cross two parents by taking a random numeric value between each of the genes"""
"""Cross two parents by taking a random numeric value between each of the genes."""
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene( # filled with new genes
@ -108,7 +108,7 @@ class Crossover_Methods:
def float_weighted(ga, parent_one, parent_two):
"""Cross two parents by taking a a weighted average of the genes"""
"""Cross two parents by taking a a weighted average of the genes."""
# the percentage of genes taken from the first gene
weight = 0.25

3
src/parent_selection/parent_selection_methods.py
View File

@ -55,7 +55,8 @@ class Parent_Selection:
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."""
the ball falls is a randomly generated number between 0 and 1.
"""
# Make sure the population is sorted by fitness
ga.population.sort_by_best_fitness(ga)