def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
controller.title('Genetic Algorithm')
canvas = tk.Canvas(self, width=canvas_width, height=canvas_height)
canvas.pack(expand=tk.YES, fill=tk.BOTH, padx=20, pady=20)
population = search.init_population(max_population, gene_pool, len(target))
for generation in range(ngen):
population = [search.mutate(search.recombine(*search.select(2, population, fitness_fn)), gene_pool, mutation_rate) for i in range(len(population))]
current_best = ''.join(argmax(population, key=fitness_fn))
members = [''.join(x) for x in population][:48]
canvas.delete('all')
canvas.create_text(canvas_width / 2, 40, fill=p_blue, font='Consolas 46 bold', text=current_best)
for i in range(len(members) // 3):
canvas.create_text((canvas_width * .175), (canvas_height * .25 + (25 * i)), fill=p_blue, font='Consolas 16', text=members[3 * i])
canvas.create_text((canvas_width * .500), (canvas_height * .25 + (25 * i)), fill=p_blue, font='Consolas 16', text=members[3 * i + 1])
canvas.create_text((canvas_width * .825), (canvas_height * .25 + (25 * i)), fill=p_blue, font='Consolas 16', text=members[3 * i + 2])
canvas.create_text((canvas_width * .5), (canvas_height * 0.95), fill=p_blue, font='Consolas 18 bold', text=f'Generation {generation}')
canvas.update()
fittest_individual = search.fitness_threshold(fitness_fn, f_thres, population)
if fittest_individual:
break
def genetic_algorithm_stepwise(population):
root.title('Genetic Algorithm')
for generation in range(ngen):
# generating new population after selecting, recombining and mutating the existing population
population = [search.mutate(search.recombine(*search.select(2, population, fitness_fn)), gene_pool, mutation_rate) for i in range(len(population))]
# genome with the highest fitness in the current generation
current_best = ''.join(argmax(population, key=fitness_fn))
# collecting first few examples from the current population
members = [''.join(x) for x in population][:48]
# clear the canvas
canvas.delete('all')
# displays current best on top of the screen
canvas.create_text(canvas_width / 2, 40, fill=p_blue, font='Consolas 46 bold', text=current_best)
# displaying a part of the population on the screen
for i in range(len(members) // 3):
canvas.create_text((canvas_width * .175), (canvas_height * .25 + (25 * i)), fill=lp_blue, font='Consolas 16', text=members[3 * i])
canvas.create_text((canvas_width * .500), (canvas_height * .25 + (25 * i)), fill=lp_blue, font='Consolas 16', text=members[3 * i + 1])
canvas.create_text((canvas_width * .825), (canvas_height * .25 + (25 * i)), fill=lp_blue, font='Consolas 16', text=members[3 * i + 2])
# displays current generation number
canvas.create_text((canvas_width * .5), (canvas_height * 0.95), fill=p_blue, font='Consolas 18 bold', text=f'Generation {generation}')
# displays blue bar that indicates current maximum fitness compared to maximum possible fitness
scaling_factor = fitness_fn(current_best) / len(target)
canvas.create_rectangle(canvas_width * 0.1, 90, canvas_width * 0.9, 100, outline=p_blue)
canvas.create_rectangle(canvas_width * 0.1, 90, canvas_width * 0.1 + scaling_factor * canvas_width * 0.8, 100, fill=lp_blue)
canvas.update()
# checks for completion
fittest_individual = search.fitness_threshold(fitness_fn, f_thres, population)
if fittest_individual:
break
def genetic_algorithm_stepwise(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1200, pmut=0.1):
for i in range(ngen):
population = [search.mutate(search.recombine(*search.select(2, population, fitness_fn)), gene_pool, pmut) for i in range(len(population))]
current_best = ''.join(argmax(population, key=fitness_fn))
print(f'Current best: {current_best}\tIteration: {str(i)}\tFitness: {fitness_fn(current_best)}\r', end='')
fittest_individual = search.fitness_threshold(fitness_fn, f_thres, population)
if fittest_individual:
return fittest_individual, i
return argmax(population, key=fitness_fn), i
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
controller.title('Genetic Algorithm')
w = tk.Canvas(self, width=800, height=600)
w.pack(expand=tk.YES, fill=tk.BOTH)
# var = tk.StringVar()
# var.set('')
# label = tk.Label(self, textvariable=var)
# label.pack()
population = search.init_population(max_population, gene_pool,
len(target))
for i in range(100):
print(i)
# time.sleep(0.2)
population = [
search.mutate(
search.recombine(
*search.select(2, population, fitness_fn)), gene_pool,
mutation_rate) for i in range(len(population))
]
current_best = ''.join(argmax(population, key=fitness_fn))
w.create_text(100,
10,
fill='darkblue',
font='Times 20 italic bold',
text=current_best)
w.update()
# var.set(current_best)
# parent.update_idletasks()
# checks for completion
fittest_individual = search.fitness_threshold(
fitness_fn, f_thres, population)
if fittest_individual:
break
# v.set(current_best)
# self.update_idletasks()
# label.configure(text=current_best)
# label.update()
# solution, generations = genetic_algorithm_stepwise(self, population, fitness_fn, gene_pool, f_thres, ngen, mutation_rate)
controller.mainloop()
def game_loop(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1200, pmut=0.1):
global generation
generation = 0
running = True
finished = False
while running:
for event in pygame.event.get():
# defining functions to execute on keypresses
if event.type == pygame.QUIT:
running = False
elif event.type == KEYDOWN and event.key == K_ESCAPE:
running = False
elif event.type == KEYDOWN and event.key == K_p:
pygame.image.save(screen, 'genetic_algorithm_phrase_gen.png') # screenshot
# set finished to True if we exceed the maximum number of generations
if generation >= ngen:
finished = True
if not finished:
screen.fill(THECOLORS['white'])
generation += 1
# generating new population after selecting, recombining and mutating the existing population
population = [search.mutate(search.recombine(*search.select(2, population, fitness_fn)), gene_pool, pmut) for i in range(len(population))]
# genome with the highest fitness in the current generation
current_best = ''.join(argmax(population, key=fitness_fn))
# checks for completion
fittest_individual = search.fitness_threshold(fitness_fn, f_thres, population)
if fittest_individual:
finished = True
# return fittest_individual
# displays current best on top of the screen
large_text = pygame.font.SysFont('Consolas', 80, bold=True)
m_text_surface, m_text_rect = text_objects(current_best, large_text, p_blue)
m_text_rect.center = ((display_width/2), (display_height * 0.1))
screen.blit(m_text_surface, m_text_rect)
# collecting first few examples from the current population
members = [''.join(x) for x in population][:48]
small_text = pygame.font.SysFont('Consolas', 20)
# displaying a part of the population on the screen
for i in range(len(members) // 3):
m_text_surface1, m_text_rect1 = text_objects(members[3*i], small_text, light_p_blue)
m_text_surface2, m_text_rect2 = text_objects(members[3*i+1], small_text, light_p_blue)
m_text_surface3, m_text_rect3 = text_objects(members[3*i+2], small_text, light_p_blue)
m_text_rect1.center = ((display_width * .175), (display_height * 0.25 + (25 * i)))
m_text_rect3.center = ((display_width * .500), (display_height * 0.25 + (25 * i)))
m_text_rect2.center = ((display_width * .825), (display_height * 0.25 + (25 * i)))
screen.blit(m_text_surface1, m_text_rect1)
screen.blit(m_text_surface2, m_text_rect2)
screen.blit(m_text_surface3, m_text_rect3)
# displays blue bar that indicates current maximum fitness compared to maximum possible fitness
scaling_factor = fitness_fn(current_best) / len(target)
pygame.draw.rect(screen, p_blue, (m_text_rect[0], m_text_rect[1] + 85, m_text_rect[2], 10), 2)
pygame.draw.rect(screen, (12 - 12 * scaling_factor, 57 - 57 *scaling_factor, 76 - 76 * scaling_factor), (m_text_rect[0], m_text_rect[1] + 85, m_text_rect[2] * scaling_factor, 10))
# displays current generation number
g_text_surface, g_text_rect = text_objects(f'Generation {generation}', pygame.font.SysFont('Consolas', 20, bold=True), light_p_blue)
g_text_rect.center = ((display_width * 0.5), (display_height * 0.95))
screen.blit(g_text_surface, g_text_rect)
else:
button('NEXT', display_width * 0.9, display_height * 0.920, display_width * 0.070, display_height * 0.05, p_blue, light_p_blue, game_intro)
# updates the screen
pygame.display.update()
clock.tick(60)