aima-python/gui/genetic_algorithm_example.py at master · pralgomathic/aima-python · GitHub

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# A simple program that implements the solution to the phrase generation problem using

# genetic algorithms as given in the search.ipynb notebook.

# Type on the home screen to change the target phrase

# Click on the slider to change genetic algorithm parameters

# Click 'GO' to run the algorithm with the specified variables

# Displays best individual of the current generation

# Displays a progress bar that indicates the amount of completion of the algorithm

# Displays the first few individuals of the current generation

import os.path

from tkinter import *

from tkinter import ttk

import search

sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

LARGE_FONT = ('Verdana', 12)

EXTRA_LARGE_FONT = ('Consolas', 36, 'bold')

canvas_width = 800

canvas_height = 600

black = '#000000'

white = '#ffffff'

p_blue = '#042533'

lp_blue = '#0c394c'

# genetic algorithm variables

# feel free to play around with these

target = 'Genetic Algorithm' # the phrase to be generated

max_population = 100 # number of samples in each population

mutation_rate = 0.1 # probability of mutation

f_thres = len(target) # fitness threshold

ngen = 1200 # max number of generations to run the genetic algorithm

generation = 0 # counter to keep track of generation number

u_case = [chr(x) for x in range(65, 91)] # list containing all uppercase characters

l_case = [chr(x) for x in range(97, 123)] # list containing all lowercase characters

punctuations1 = [chr(x) for x in range(33, 48)] # lists containing punctuation symbols

punctuations2 = [chr(x) for x in range(58, 65)]

punctuations3 = [chr(x) for x in range(91, 97)]

numerals = [chr(x) for x in range(48, 58)] # list containing numbers

# extend the gene pool with the required lists and append the space character

gene_pool = []

gene_pool.extend(u_case)

gene_pool.extend(l_case)

gene_pool.append(' ')

# callbacks to update global variables from the slider values

def update_max_population(slider_value):

global max_population

max_population = slider_value

def update_mutation_rate(slider_value):

global mutation_rate

mutation_rate = slider_value

def update_f_thres(slider_value):

global f_thres

f_thres = slider_value

def update_ngen(slider_value):

global ngen

ngen = slider_value

# fitness function

def fitness_fn(_list):

fitness = 0

# create string from list of characters

phrase = ''.join(_list)

# add 1 to fitness value for every matching character

for i in range(len(phrase)):

if target[i] == phrase[i]:

fitness += 1

return fitness

# function to bring a new frame on top

def raise_frame(frame, init=False, update_target=False, target_entry=None, f_thres_slider=None):

frame.tkraise()

global target

if update_target and target_entry is not None:

target = target_entry.get()

f_thres_slider.config(to=len(target))

if init:

population = search.init_population(max_population, gene_pool, len(target))

genetic_algorithm_stepwise(population)

# defining root and child frames

root = Tk()

f1 = Frame(root)

f2 = Frame(root)

# pack frames on top of one another

for frame in (f1, f2):

frame.grid(row=0, column=0, sticky='news')

# Home Screen (f1) widgets

target_entry = Entry(f1, font=('Consolas 46 bold'), exportselection=0, foreground=p_blue, justify=CENTER)

target_entry.insert(0, target)

target_entry.pack(expand=YES, side=TOP, fill=X, padx=50)

target_entry.focus_force()

max_population_slider = Scale(f1, from_=3, to=1000, orient=HORIZONTAL, label='Max population',

command=lambda value: update_max_population(int(value)))

max_population_slider.set(max_population)

max_population_slider.pack(expand=YES, side=TOP, fill=X, padx=40)

mutation_rate_slider = Scale(f1, from_=0, to=1, orient=HORIZONTAL, label='Mutation rate', resolution=0.0001,

command=lambda value: update_mutation_rate(float(value)))

mutation_rate_slider.set(mutation_rate)

mutation_rate_slider.pack(expand=YES, side=TOP, fill=X, padx=40)

f_thres_slider = Scale(f1, from_=0, to=len(target), orient=HORIZONTAL, label='Fitness threshold',

command=lambda value: update_f_thres(int(value)))

f_thres_slider.set(f_thres)

f_thres_slider.pack(expand=YES, side=TOP, fill=X, padx=40)

ngen_slider = Scale(f1, from_=1, to=5000, orient=HORIZONTAL, label='Max number of generations',

command=lambda value: update_ngen(int(value)))

ngen_slider.set(ngen)

ngen_slider.pack(expand=YES, side=TOP, fill=X, padx=40)

button = ttk.Button(f1, text='RUN',

command=lambda: raise_frame(f2, init=True, update_target=True, target_entry=target_entry,

f_thres_slider=f_thres_slider)).pack(side=BOTTOM, pady=50)

# f2 widgets

canvas = Canvas(f2, width=canvas_width, height=canvas_height)

canvas.pack(expand=YES, fill=BOTH, padx=20, pady=15)

button = ttk.Button(f2, text='EXIT', command=lambda: raise_frame(f1)).pack(side=BOTTOM, pady=15)

# function to run the genetic algorithm and update text on the canvas

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(max(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

raise_frame(f1)

root.mainloop()

aima-python/gui/genetic_algorithm_example.py at master · pralgomathic/aima-python · GitHub

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