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gui/genetic_algorithm_example.py

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+# author: ad71
+# 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 sys
+import time
+import random
+import os.path
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+
+from tkinter import *
+from tkinter import ttk
+
+import search
+from utils import argmax
+
+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(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
+
+raise_frame(f1)
+root.mainloop()