Create genetic_algorithm_example.py

Author: Cat-Gawr

src_A2/gui/genetic_algorithm_example.py

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+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()