def __init__(self):
self.frame_rate = 10000 # FPS
self.frames = 0 # counts number of frames elapsed
self.exit = False # Flag to exit the game
self.pause = False
self.manual = False
self.screen_on = True
title = 'Snake' # Window Title
icon_filename = 'res/icon.png'
# loads pygame modules
pygame.init()
# initializing game objects
self.population = Population(layers=(12, 16, 16, 3))
# screen params
icon = pygame.image.load(icon_filename)
pygame.display.set_icon(icon)
pygame.display.set_caption(title)
self.screen = pygame.display.set_mode(Ground().get_dimensions())
# reference clock
self.clock = pygame.time.Clock()
Game.__instance__ = self
def __init__(self):
# layers used for neural network
self.layers = (4, 64, 64, 64, 1)
# initializing population for genetic algorithm
self.population = Population(layers=self.layers)
# initializing openai gym environment for cartpole game
self.env = gym.make('CartPole-v0')
def __init__(self):
# layers used for neural network
self.layers = (2, 8, 8, 8, 3)
# initializing population for genetic algorithm
self.population = Population(layers=self.layers)
# initializing openai gym environment for cartpole game
self.env = gym.make('MountainCar-v0')
self.episode_threshold = 300
def evolve(world: World, population: Population):
new_generation = Population(population.population_size, world.gene_length,
False, [])
elitism_number = population.population_size // 2
#Elitism
for _ in range(elitism_number):
fittest = population.getFittest()
new_generation.addIndividual(fittest)
population.removeIndividual(fittest)
#Crossover
for _ in range(elitism_number, population.population_size):
parent1 = selection(new_generation, world)
parent2 = selection(new_generation, world)
child = crossover(parent1, parent2)
child.setFitness(
fitnessFN(world, child.path, world.startX, world.startY,
world.foodCount))
new_generation.addIndividual(child)
#Mutation
for i in range(elitism_number, population.population_size):
mutation(new_generation.individuals[i], world.mutation_rate)
return new_generation
def genetic_algorithm(self, population_size, generations, mutation):
population = Population(self.estimate_all_ages,
n=population_size,
mutation=mutation,
params_size=self.params.size)
for i in range(generations):
population.evolve()
with open(
"ga_" + str(population_size) + "_" + str(generations) + "_" +
str(mutation) + ".log", "w+") as file:
print("#generation, best_fitness_of_generation, best_params",
file=file)
for i in range(generations):
print(str(i + 1) + " " + str(population.best_fitnesses[i]) +
" " + get_nice_string(population.best_params[i]),
file=file)
def initialize():
# initializes modules required for pygame
pygame.init()
# screen parameters
icon = pygame.image.load(Game.ICON_PATH)
pygame.display.set_icon(icon)
pygame.display.set_caption(Game.TITLE)
screen = pygame.display.set_mode(Game.get_dimensions())
# Feature limits for neural network's normalization
feature_limits = array([Game.HEIGHT, 100, Game.HEIGHT, Game.HEIGHT, Game.WIDTH])
Game.feature_limits = reshape(feature_limits, (1,-1))
# reference clock
clock = pygame.time.Clock()
# initializing game objects
population = Population(pop_size=30, feature_limits=Game.feature_limits) # population of birds
pipes = [] # pipes on screen
# background image
background_image = pygame.image.load('res/background.png').convert()
return screen, clock, population, pipes, background_image
class Game:
def __init__(self):
# layers used for neural network
self.layers = (2, 8, 8, 8, 3)
# initializing population for genetic algorithm
self.population = Population(layers=self.layers)
# initializing openai gym environment for cartpole game
self.env = gym.make('MountainCar-v0')
self.episode_threshold = 300
def loop(self):
""" loops infinitely for training """
high_score = -999999 # used to store high score in training so far
# learn infinitely
while True:
# iterating individuals in population
for individual in self.population.individuals:
X = self.env.reset() # Feature vector
done = False # flag set by gym when game is over
episode_length = 0
while not done and episode_length < self.episode_threshold:
# self.env.render()
y = individual.nn.feed_forward(X)
X, reward, done, info = self.env.step(argmax(y))
individual.score = X[0]
episode_length += 1
# save model when new high score is achieved
if individual.score > high_score:
data = {
'score': individual.score,
'number_of_layers': individual.nn.number_of_layers,
'weights': individual.nn.weights,
}
# write to file
with open('model.pkl', 'wb') as f:
pickle.dump(data, f)
high_score = individual.score
self.population.evolve()
def main():
max_generations = 100
max_not_improved = 10
# Creating initial population
pop = Population( size = 100,
crossover = 0.4,
mutation = 0.05,
elitism = 0.05,
imigration = 0.3,
tournament_size = 10,
debug = False)
# Generations without improvements
no_improvements = 0
for i in range(max_generations):
#print 'Generation %d' %(i)
print 'Calculating fitness'
pop.evaluate()
pop.plot_evolution()
if not pop.improved:
no_improvements += 1
print "Didn't improve:", no_improvements
else:
no_improvements = 0
if no_improvements == max_not_improved:
break
print 'Evolving'
pop.evolve()
print
if no_improvements == max_not_improved:
print '%d generations without improvement' % (max_not_improved)
print 'Best solution:'
pop.show_first()
class Game:
def __init__(self):
# layers used for neural network
self.layers = (4, 64, 64, 64, 1)
# initializing population for genetic algorithm
self.population = Population(layers=self.layers)
# initializing openai gym environment for cartpole game
self.env = gym.make('CartPole-v0')
def loop(self):
""" loops infinitely for training """
high_score = 0 # used to store high score in training so far
# learn infinitely
while True:
# iterating individuals in population
for individual in self.population.individuals:
X = self.env.reset() # Feature vector
done = False # flag set by gym when game is over
while not done:
# self.env.render()
y = 1 if individual.nn.feed_forward(X)[0] > 0.5 else 0
X, reward, done, info = self.env.step(y)
individual.score += reward
# save model when new high score is achieved
if individual.score > high_score:
data = {
'score' : individual.score,
'number_of_layers' : individual.nn.number_of_layers,
'weights' : individual.nn.weights,
}
# write to file
with open('model.pkl', 'wb') as f:
pickle.dump(data, f)
high_score = individual.score
self.population.evolve()
def main():
max_generations = 100
max_not_improved = 10
# Creating initial population
pop = Population( size = 100,
crossover = 0.4,
mutation = 0.05,
elitism = 0.05,
imigration = 0.3,
tournament_size = 10,
debug = False)
# Generations without improvements
no_improvements = 0
for i in range(max_generations):
print 'Generation %d' % (i+1)
print 'Calculating fitness'
pop.evaluate()
pop.plot_evolution()
if not pop.improved:
no_improvements += 1
print "Didn't improve:", no_improvements
else:
no_improvements = 0
if no_improvements == max_not_improved:
break
print 'Evolving'
pop.evolve()
print
if no_improvements == max_not_improved:
print '%d generations without improvement' % (max_not_improved)
print 'Best solution:'
pop.show_first()
def initialize():
pygame.init()
# screen parameters
icon = pygame.image.load(Game.ICON_PATH)
pygame.display.set_icon(icon)
pygame.display.set_caption(Game.TITLE)
screen = pygame.display.set_mode(Game.get_dimensions())
# Feature limits
feature_limits = array(
[Game.HEIGHT, 100, Game.HEIGHT, Game.HEIGHT, Game.WIDTH])
Game.feature_limits = reshape(feature_limits, (1, -1))
# reference clock
clock = pygame.time.Clock()
# initializing game objects
population = Population(pop_size=30,
feature_limits=Game.feature_limits)
pipes = []
return screen, clock, population, pipes
from attribute import Attribute
from dataset import Dataset
from genetic import Population
attributes = [Attribute("score", [x/10 for x in range(-50, 50)]),]
data = [[x/10] for x in range(-50, 50)] * 5
scores = Population(Dataset(attributes, data), lambda i: -i[0]**4 + i[0]**3 + 20*i[0]**2 + i[0]-2)
print("using -x^4+x^3+20x^2+x-2\n")
print("initial",scores)
for i in range(1000):
scores.generation()
print("final",scores)
print("frequency", scores.frequency())
print("mode",scores.mode())
print("fittest",scores.fittest())
def test_selection(self, *args):
population = Population(self.buildings_template)
population.selection()
self.assertEqual(45, len(population.creatures))
class Game:
__instance__ = None
def get_instance():
if Game.__instance__ == None:
Game()
return Game.__instance__
def __init__(self):
self.frame_rate = 10000 # FPS
self.frames = 0 # counts number of frames elapsed
self.exit = False # Flag to exit the game
self.pause = False
self.manual = False
self.screen_on = True
title = 'Snake' # Window Title
icon_filename = 'res/icon.png'
# loads pygame modules
pygame.init()
# initializing game objects
self.population = Population(layers=(12, 16, 16, 3))
# screen params
icon = pygame.image.load(icon_filename)
pygame.display.set_icon(icon)
pygame.display.set_caption(title)
self.screen = pygame.display.set_mode(Ground().get_dimensions())
# reference clock
self.clock = pygame.time.Clock()
Game.__instance__ = self
def loop(self):
while not self.exit:
for individual in self.population.individuals:
snake = individual.snake
while not self.exit and not snake.game_over:
# capture user input
self.capture_input(snake)
if self.pause:
continue
# update objects for each frame
self.update_objects(snake)
# if game over skip
if snake.game_over:
distance_from_food = snake.delta_distance
snake.score -= distance_from_food
break
# get feature vector
X = snake.get_features()
# output of feed forward of neural network
y = ravel(individual.nn.feed_forward(X))
snake.respond(y)
if self.screen_on:
# draw objects on screen
self.draw_objects(snake)
# print(individual.nn.weights)
self.population.evolve()
def update_objects(self, snake):
snake.update()
def capture_input(self, snake):
for event in pygame.event.get():
if event.type == pygame.QUIT:
self.exit = True
elif event.type == pygame.KEYDOWN:
if self.manual:
if event.key == pygame.K_UP:
snake.move(Direction.UP)
elif event.key == pygame.K_DOWN:
snake.move(Direction.DOWN)
elif event.key == pygame.K_LEFT:
snake.move(Direction.LEFT)
elif event.key == pygame.K_RIGHT:
snake.move(Direction.RIGHT)
# keys to control frame rate
# u: 10000
# i: 100
# o: 10
# p: 1
if event.key == pygame.K_u:
self.frame_rate = 10000
if event.key == pygame.K_i:
self.frame_rate = 100
if event.key == pygame.K_o:
self.frame_rate = 10
if event.key == pygame.K_p:
self.frame_rate = 1
# on pressing q current snake's game is over
if event.key == pygame.K_q:
snake.game_over = True
# s: draws screen
# a: stops drawing on screen
if event.key == pygame.K_a:
self.screen_on = False
if event.key == pygame.K_s:
self.screen_on = True
# k: toggles pause
if event.key == pygame.K_k:
self.pause = not self.pause
def draw_objects(self, snake):
self.screen.fill(Color.BLACK)
snake.draw(self.screen)
pygame.display.update()
self.clock.tick(self.frame_rate)
from genetic import Population
population_size = 100
mutation_rate = 0.01
target_word = "The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge."
p = Population(population_size, mutation_rate, target_word)
while p.get_best().fitness < 1.0:
print(''.join(p.get_best().genotype))
p.natural_selection()
print(''.join(p.get_best().genotype))
def test_random_population(self):
population = Population(self.buildings_template)
self.assertEqual(45, len(population.creatures))
def setupWorld(self, world):
for goal in self.goals:
population = Population(self.setupPopulation, self.selection,
self.regulation, self.fitness, goal)
world.populations.append(population)
from attribute import Attribute
from dataset import Dataset
from genetic import Population
def fitness(i):
return 50 - i[0] ** 2 - i[1] ** 2
i = [x for x in range(-5, 6)]
attributes = [Attribute("x", i), Attribute("y", i)]
data = [[x, y] for x in i for y in i] * 5
final = Population(Dataset(attributes, data), fitness, 1000)
print(final.population)
final.summary()
import cupy as cp
from genetic import Population
# 生成初始种群，种群有 pop_size 个个体，每个个体有 problem_size 个基因位点
pop_size = 100
problem_size = 12
population = Population.generate_initial_population(pop_size, problem_size)
distance_mat = cp.random.randint(low=1,
high=1000,
size=(
problem_size,
problem_size,
)).astype(cp.float32)
max_evolves_n = 100
for i in range(max_evolves_n):
population.evolve(distance_mat)
population.update_chance(distance_mat)
print("At generation: %s\n Score: %s\n" % (
str(i),
population.get_maximum_score(),
))
def main():
"""Run the evolutionary algorithm"""
# Command line options
formatter = IndentedHelpFormatter(indent_increment=4, max_help_position=40, width=80)
parser = OptionParser(usage="%prog -f INFILE -o OUTDIR [other opts]",
version="%prog 0.1", formatter=formatter)
# File options
group = OptionGroup(parser, "File and Directory Options")
group.add_option("-f", "--file", dest = "filename",
default = None,
help = "Input file to evolve to", metavar = "FILE")
group.add_option("-o", "--outImg", dest = "imgOutputDir",
default = "./out",
help = "Directory to save output images in",
metavar = "DIR")
group.add_option("-p", "--outObj", dest = "objOutputDir",
default = "./obj",
help = "Directory to save serialized objects in",
metavar = "DIR")
group.add_option("-z", "--loadObjs", dest = "loadObjs",
default = False,
help = "Load objects from a previous generation?",
metavar = "True")
parser.add_option_group(group)
# Individual Options
group = OptionGroup(parser, "Individual Options",
"These options affect how individuals evolve")
group.add_option("-i", "--iGenes", dest = "initGenes",
default = 5,
help = "Initial number of genes", metavar = "NUM")
group.add_option("-m", "--mGenes", dest = "maxGenes",
default = 60,
help = "Maximum number of genes", metavar = "NUM")
group.add_option("-x", "--iMutation", dest = "initMutate",
default = 5,
help = "Number of mutations for initial generation",
metavar = "NUM")
parser.add_option_group(group)
# Population Options
group = OptionGroup(parser, "Population Options",
"These options affect the population dynamics")
group.add_option("-s", "--iSize", dest = "initSize",
default = 5,
help = "Initial size of the population", metavar = "SIZE")
group.add_option("-l", "--mSize", dest = "maxSize",
default = 40,
help = "Maximum size of the population", metavar = "SIZE")
parser.add_option_group(group)
# Get args
(options, args) = parser.parse_args()
if not options.filename:
print "Did not supply -f INPUT FILE. Check the help docs with --help."
return
print "DEBUG - NUMBER OF GENES SET STATIC!"
genes = int(options.initGenes)
# Begin evolution
P = Population("TODO",
options.filename,
options.imgOutputDir,
options.objOutputDir,
loadObjs = bool(options.loadObjs),
initSize = int(options.initSize),
maxSize = int(options.maxSize),
initGenes = genes,
maxGenes = genes,
initMutate = int(options.initMutate))
P.runEvolution()
def main(filename=None, ports=[5000]):
if not filename:
print 'Using default parameters'
params = {
'max_generations': 200,
'max_not_improved': 20,
'size': 100,
'crossover': 0.3,
'mutation': 0.05,
'elitism': 0.2,
'imigration': 0.2,
'tour_size': 8,
'local': None
}
else:
print 'Loading parameters from %s\n' % (filename)
params = util.parse_json(filename)
# Creating initial population
pop = Population(size=params['size'],
crossover=params['crossover'],
mutation=params['mutation'],
elitism=params['elitism'],
imigration=params['imigration'],
tour_size=params['tour_size'],
local=params['local'])
sockets = []
for port in ports:
sock = util.open_socket(int(port))
sockets.append(sock)
Chromo.sockets = sockets
Chromo.cross_op = params['cross_op']
# Generations without improvements
no_improv = 0
for i in range(params['max_generations']):
print 'Generation %d' % (i + 1)
print 'Calculating fitness'
for sock in Chromo.sockets:
util.send_msg(sock, 'NEWGEN\n')
best = pop.evaluate()
for sock in Chromo.sockets:
util.send_msg(sock, 'ENDGEN\n')
print 'main:current_best ', best
#pop.plot_evolution()
if not pop.improved:
no_improvements += 1
print "Didn't improve:", no_improvements
if no_improvements == params['max_not_improved']:
print 'Reached limit for not improved generations'
break
else:
no_improvements = 0
print 'Evolving'
pop.evolve()
print
print '\nBest solution:'
#pop.show_first()
print 'Fitness: ', best[0]
print 'Genes: ', Chromo.to_str(best[1]), '\''
# Closing connection
for sock in Chromo.sockets:
util.send_msg(sock, 'ENDGA\n')
sock.close()
from genetic import Population # population with 100 individuals, 4 traits each p = Population(100, 4) # initialize all traits to value 15 p.init_population(15) # set targets for each trait p.set_targets([10, 20, 15, 25]) # run 100 generations p.run_generations(100) # plot the history of the traits on a 2x2 grid p.plot_history((2,2))
import sys
import ast
import json
import test
from genetic import Population
if __name__ == '__main__':
argu1 = ast.literal_eval(sys.argv[1])
#argu1 = sys.argv[1]
#argu2 = ast.literal_eval(sys.argv[2])
#print(argu1)
path = '/common/users/sl1560/temp/'
name = 'aStar+120+0.1+500.pkl'
p = Population(**argu1)
pp = test.loadMaze(path, name)
for i in range(1):
p.replaceInitialMazes(i, pp[i])
print('changed', pp[i].score)
finalchild, finalPopulation = p.iterate()
p.save()
#finalchild.visualize()
#p = Population(**sys.argv[1])
#arguments = sys.argv[1].replace("'", "\"")
#p = Population(json.loads(arguments))
def run(args):
startX = args.startx
startY = args.starty
foodCount = args.food_count
world = World(args.world_size, args.startx, args.starty, args.food_count,
args.mut_rate)
#Generating the Population
population = Population(args.population_size, world.gene_length, True, [])
start_time = time.time()
elapsed_time = 0
total_fitness = 0
max_fitness = 0
max_fitness_index = 0
i = 0
for individual in population.individuals:
fit = fitnessFN(world, individual.path, startX, startY, foodCount)
individual.setFitness(fit)
total_fitness += fit
if fit > max_fitness:
max_fitness = fit
max_fitness_index = i
i += 1
best_individual = copy.copy(population.individuals[max_fitness_index])
avg_fitness = total_fitness / population.population_size
total_generations = args.max_generation_number
generation_counter = 0
while generation_counter < total_generations and best_individual.fitness < world.foodCount:
print("Generation : ", generation_counter)
print("Max fitness: ", best_individual.fitness)
print("Average fitness: ", avg_fitness)
population = evolve(world, population)
new_best_individual = population.getFittest()
avg_fitness = population.calculateAverageFitness()
if new_best_individual.fitness > best_individual.fitness:
generation_counter, best_individual = 0, new_best_individual
else:
generation_counter += 1
total_generations = 0
end_time = time.time()
elapsed_time = end_time - start_time
print("\n-------------------------------\nElapsed Time: ", elapsed_time,
" sec")
print("Population Size: ", args.population_size, " World Size: ",
args.world_size, "\nStart x, y:", args.startx, ",", args.starty,
" Food Count: ", args.food_count, "\nMutation Rate: ", args.mut_rate)
print("Result: ", best_individual.toString())
matrix = traverse(best_individual.path, world)
draw_matrix(matrix, 'title')
from attribute import Attribute
from dataset import Dataset
from genetic import Population
attributes = [Attribute("score", list(range(-5,6))),]
data = [
[5],
[4],
[3],
[2],
[1],
[0],
[-1],
[-2],
[-3],
[-4],
[-5],
]
data = data * 10
scores = Population(Dataset(attributes, data), lambda i: 5 - abs(i[0]), rseed=1)
print(scores)
for i in range(100):
scores.generation()
print(scores)
print(scores.frequency())
