class EAGui(tk.Tk):
def __init__(self, delay, nr_of_runs=1, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs)
self.delay = delay
self.nr_of_runs = nr_of_runs
self.current_run = 0
self.current_generation = 0
self.fitness_log_average = []
self.fitness_log_best = []
self.standard_deviation_log = []
self.center_window()
self.beertracker_worlds = None
self.ea = None
self.build_parameter_menu()
def center_window(self):
ws = self.winfo_screenwidth() # width of the screen
hs = self.winfo_screenheight() # height of the screen
w = 1000
h = 900
x = (ws/2) - (w/2)
y = (hs/2) - (h/2)
self.geometry('%dx%d+%d+%d' % (w, h, x, y))
def build_parameter_menu(self):
self.horizontal_slider_1 = tk.Scale(self, length=1000, from_=2, to=1000, orient=tk.HORIZONTAL,
label="Genotype Pool Size", resolution=2,
command=self.control_pool_size_genotype)
self.horizontal_slider_1.set(GENOTYPE_POOL_SIZE)
self.horizontal_slider_1.pack()
self.horizontal_slider_2 = tk.Scale(self, length=1000, from_=2, to=1000, orient=tk.HORIZONTAL,
label="Adult Pool Size", resolution=2, command=self.control_pool_size_adult)
self.horizontal_slider_2.set(ADULT_POOL_SIZE)
self.horizontal_slider_2.pack()
self.horizontal_slider_3 = tk.Scale(self, length=1000, from_=0, to=1000, orient=tk.HORIZONTAL,
label="Elitism", resolution=1, command=self.control_pool_size_adult)
self.horizontal_slider_3.set(ELITISM)
self.horizontal_slider_3.pack()
ann_entry_group = tk.Frame(self)
tk.Label(ann_entry_group, text="Hidden layers:").pack(side=tk.LEFT)
self.hidden_layers = tk.StringVar()
self.hidden_layers.set("3")
tk.Entry(ann_entry_group, textvariable=self.hidden_layers).pack(side=tk.LEFT)
tk.Label(ann_entry_group, text="Activation functions (0:Sigmoid, 1:Hyperbolic tangent, 2:Rectify, 3:Softmax):",
padx=20).pack(side=tk.LEFT)
self.activation_functions = tk.StringVar()
self.activation_functions.set("0, 0")
tk.Entry(ann_entry_group, textvariable=self.activation_functions).pack(side=tk.LEFT)
ann_entry_group.pack(anchor=tk.W)
crossover_slider_group = tk.Frame(self)
self.horizontal_slider_4 = tk.Scale(crossover_slider_group, length=500, from_=0.0, to=1.0, resolution=-1,
orient=tk.HORIZONTAL, label="Crossover Rate")
self.horizontal_slider_4.set(CROSSOVER_RATE)
self.horizontal_slider_4.pack(side=tk.LEFT)
self.horizontal_slider_5 = tk.Scale(self, length=1000, from_=0, to=1, resolution=-1, orient=tk.HORIZONTAL,
label="Mutation Rate")
self.horizontal_slider_5.set(MUTATION_RATE)
self.horizontal_slider_5.pack()
self.horizontal_slider_6 = tk.Scale(crossover_slider_group, length=500, from_=1, to=100, orient=tk.HORIZONTAL,
label="Points of Crossover")
self.horizontal_slider_6.set(POINTS_OF_CROSSOVER)
self.horizontal_slider_6.pack(side=tk.LEFT)
crossover_slider_group.pack(anchor=tk.W)
self.horizontal_slider_7 = tk.Scale(self, length=1000, from_=1, to=10000, orient=tk.HORIZONTAL,
label="Symbol Set Size")
self.horizontal_slider_7.set(SYMBOL_SET_SIZE)
self.horizontal_slider_7.pack(anchor=tk.W)
tournament_slider_group = tk.Frame(self)
self.horizontal_slider_8 = tk.Scale(tournament_slider_group, length=500, from_=3, to=500, orient=tk.HORIZONTAL,
label="Tournament Size")
self.horizontal_slider_8.set(TOURNAMENT_SIZE)
self.horizontal_slider_8.pack(side=tk.LEFT)
self.horizontal_slider_9 = tk.Scale(tournament_slider_group, length=500, from_=0, to=1, resolution=-1,
orient=tk.HORIZONTAL, label="Tournament Slip-through Probability")
self.horizontal_slider_9.set(TOURNAMENT_SLIP_THROUGH_PROBABILITY)
self.horizontal_slider_9.pack(side=tk.LEFT)
tournament_slider_group.pack(anchor=tk.W)
self.horizontal_slider_10 = tk.Scale(self, length=1000, from_=1, to=1000, orient=tk.HORIZONTAL,
label="Initial Temperature")
self.horizontal_slider_10.set(INITIAL_TEMPERATURE)
# self.horizontal_slider_10.pack()
self.horizontal_slider_11 = tk.Scale(self, length=1000, from_=1, to=20, orient=tk.HORIZONTAL,
label="Nr of Scenarios")
self.horizontal_slider_11.set(NR_OF_SCENARIOS)
self.horizontal_slider_11.pack(anchor=tk.W)
self.horizontal_slider_12 = tk.Scale(self, length=1000, from_=1, to=500, orient=tk.HORIZONTAL,
label="Max Number of Generations")
self.horizontal_slider_12.set(MAX_GENERATIONS)
self.horizontal_slider_12.pack()
self.scenario_protocol = tk.IntVar()
self.scenario_protocol.set(SCENARIO_PROTOCOL)
scenario_protocol_group = tk.Frame(self)
tk.Label(scenario_protocol_group, text="Scenario Protocol").pack(anchor=tk.W)
tk.Radiobutton(scenario_protocol_group, text="Static", variable=self.scenario_protocol,
value=1).pack(side=tk.LEFT)
tk.Radiobutton(scenario_protocol_group, text="Dynamic", variable=self.scenario_protocol,
value=2).pack(side=tk.LEFT)
tk.Radiobutton(scenario_protocol_group, text="Static, test on random", variable=self.scenario_protocol,
value=3).pack(side=tk.LEFT)
scenario_protocol_group.pack(anchor=tk.W)
self.agent_type = tk.IntVar()
self.agent_type.set(AGENT_TYPE)
agent_type_group = tk.Frame(self)
tk.Label(agent_type_group, text="Agent Type").pack(anchor=tk.W)
tk.Radiobutton(agent_type_group, text="Standard", variable=self.agent_type,
value=1).pack(side=tk.LEFT)
tk.Radiobutton(agent_type_group, text="No wrap", variable=self.agent_type,
value=2).pack(side=tk.LEFT)
tk.Radiobutton(agent_type_group, text="Pull", variable=self.agent_type,
value=3).pack(side=tk.LEFT)
agent_type_group.pack(anchor=tk.W)
self.mutation_protocol = tk.IntVar()
self.mutation_protocol.set(MUTATION_PROTOCOL)
mutation_protocol_group = tk.Frame(self)
tk.Label(mutation_protocol_group, text="Mutation Protocol").pack(anchor=tk.W)
tk.Radiobutton(mutation_protocol_group, text="Individual", variable=self.mutation_protocol,
value=1).pack(side=tk.LEFT)
tk.Radiobutton(mutation_protocol_group, text="Component", variable=self.mutation_protocol,
value=2).pack(side=tk.LEFT)
mutation_protocol_group.pack(anchor=tk.W)
self.adult_selection_protocol = tk.IntVar()
self.adult_selection_protocol.set(ADULT_SELECTION_PROTOCOL)
adult_selection_group = tk.Frame(self)
tk.Label(adult_selection_group, text="Adult Selection Protocol").pack(anchor=tk.W)
tk.Radiobutton(adult_selection_group, text="Full", variable=self.adult_selection_protocol,
command=self.control_pool_size_adult, value=1).pack(side=tk.LEFT)
tk.Radiobutton(adult_selection_group, text="Over Production", variable=self.adult_selection_protocol,
command=self.control_pool_size_adult, value=2).pack(side=tk.LEFT)
tk.Radiobutton(adult_selection_group, text="Mixing", variable=self.adult_selection_protocol,
value=3).pack(side=tk.LEFT)
adult_selection_group.pack(anchor=tk.W)
self.parent_selection_protocol = tk.IntVar()
self.parent_selection_protocol.set(PARENT_SELECTION_PROTOCOL)
parent_selection_group = tk.Frame(self)
tk.Label(parent_selection_group, text="Parent Selection Protocol").pack(anchor=tk.W)
tk.Radiobutton(parent_selection_group, text="Fitness Proportionate", variable=self.parent_selection_protocol,
value=1).pack(side=tk.LEFT)
tk.Radiobutton(parent_selection_group, text="Sigma-scaling", variable=self.parent_selection_protocol,
value=2).pack(side=tk.LEFT)
tk.Radiobutton(parent_selection_group, text="Tournament selection", variable=self.parent_selection_protocol,
value=3).pack(side=tk.LEFT)
tk.Radiobutton(parent_selection_group, text="Boltzmann Selection", variable=self.parent_selection_protocol,
value=4).pack(side=tk.LEFT)
parent_selection_group.pack(anchor=tk.W)
start_button = tk.Button(self, text="Start", width=20, command=self.start_simulation)
start_button.pack()
def control_pool_size_genotype(self, event=None):
if self.adult_selection_protocol.get() == 1:
self.horizontal_slider_2.set(self.horizontal_slider_1.get())
elif self.adult_selection_protocol.get() == 2:
self.horizontal_slider_2.set(min(self.horizontal_slider_2.get(), self.horizontal_slider_1.get() - 2))
def control_pool_size_adult(self, event=None):
if self.adult_selection_protocol.get() == 1:
self.horizontal_slider_1.set(self.horizontal_slider_2.get())
elif self.adult_selection_protocol.get() == 2:
self.horizontal_slider_1.set(max(self.horizontal_slider_1.get(), self.horizontal_slider_2.get() - 6))
if self.parent_selection_protocol.get() == 3:
self.horizontal_slider_8.set(min(self.horizontal_slider_8.get(), self.horizontal_slider_2.get() // 2))
self.horizontal_slider_3.set(min(self.horizontal_slider_3.get(), self.horizontal_slider_2.get()))
def start_simulation(self):
self.current_run = 0
self.fitness_log_average = []
self.fitness_log_best = []
self.standard_deviation_log = []
self.start_ea()
def start_ea(self):
self.parse_ann_input()
self.ea = EvolutionaryAlgorithm(genotype_pool_size=self.horizontal_slider_1.get(),
adult_pool_size=self.horizontal_slider_2.get(),
elitism=self.horizontal_slider_3.get(),
genotype_length=self.genotype_size,
phenotype_length=self.genotype_size,
adult_selection_protocol=self.adult_selection_protocol.get(),
parent_selection_protocol=self.parent_selection_protocol.get(),
crossover_rate=self.horizontal_slider_4.get(),
mutation_rate=self.horizontal_slider_5.get(),
mutation_protocol=self.mutation_protocol.get(),
points_of_crossover=self.horizontal_slider_6.get(),
symbol_set_size=self.horizontal_slider_7.get(),
tournament_size=self.horizontal_slider_8.get(),
tournament_slip_through_probability=self.horizontal_slider_9.get(),
initial_temperature=self.horizontal_slider_10.get(),
hidden_layers=self.layers_list,
activation_functions=self.activations_list,
generations=self.horizontal_slider_12.get())
self.beertracker_worlds = [self.initialize_board() for _ in range(self.horizontal_slider_11.get())]
self.current_generation = 0
self.fitness_log_average.append([])
self.fitness_log_best.append([])
self.standard_deviation_log.append([])
self.timer = time()
self.run_ea()
def run_ea(self):
self.ea.run_one_life_cycle(self.beertracker_worlds)
if self.current_generation == 0:
self.timer = time() - self.timer
self.current_generation += 1
self.write_to_log()
self.reset_scenarios()
if self.current_generation < self.horizontal_slider_12.get() and \
self.ea.phenotype_adult_pool[0].fitness_value < 1.0:
self.after(self.delay, lambda: self.run_ea())
else:
self.current_run += 1
if self.current_run < self.nr_of_runs:
print "Current run", self.current_run
self.start_ea()
else:
self.end_ea_run_visualisation()
def end_ea_run_visualisation(self):
print "Avg best fitness:", sum([self.fitness_log_best[i][-1] for i in range(len(self.fitness_log_best))]) / \
len(self.fitness_log_best)
print "Best fitness:", max([self.fitness_log_best[i][-1] for i in range(len(self.fitness_log_best))])
BeerTrackerGui(
phenotype=self.ea.phenotype_adult_pool[0],
environments=self.beertracker_worlds,
fitness_log_average=self.fitness_log_average,
fitness_log_best=self.fitness_log_best,
standard_deviation_log=self.standard_deviation_log)
print "Average Generations:", \
sum([len(l) for l in self.fitness_log_average]) / len(self.fitness_log_average)
def initialize_board(self):
beertracker_world = BeerTrackerWorld(width=WORLD_WIDTH, height=WORLD_HEIGHT, agent_type=self.agent_type.get())
return beertracker_world
def reset_scenarios(self):
if self.scenario_protocol.get() == 1:
for world in self.beertracker_worlds:
world.reset()
else:
self.beertracker_worlds = [self.initialize_board() for _ in range(len(self.beertracker_worlds))]
def parse_ann_input(self):
if self.hidden_layers.get() != '':
self.layers_list = [INPUT_NODES] + \
map(int, self.hidden_layers.get().replace(" ", "").split(",")) + \
[OUTPUT_NODES]
else:
self.layers_list = [INPUT_NODES, OUTPUT_NODES]
if not ONE_HOT_OUTPUT:
self.layers_list[-1] = 2
# Two extra nodes for detecting walls
if self.agent_type.get() == 2:
self.layers_list[0] += 2
if self.agent_type.get() == 3:
self.layers_list[-1] += 1
if CENTERED_NODE:
self.layers_list[0] += 1
if FULL_NODE:
self.layers_list[0] += 1
self.activations_list = map(int, str(self.activation_functions.get()).replace(" ", "").split(","))
self.genotype_size = sum([self.layers_list[i] * self.layers_list[i + 1]
for i in range(len(self.layers_list) - 1)])
if RECURENCE:
for i in range(1, len(self.layers_list)):
self.genotype_size += pow(self.layers_list[i], 2)
# A Bias, time constant and gains value for each non-input neuron
self.genotype_size += sum(self.layers_list[1:]) * 3
def write_to_log(self):
print "Gen:", "%.2d" % self.current_generation, "\tBest fitness:", \
"%.3f" % round(self.ea.phenotype_adult_pool[0].fitness_value, 3), "\tAverage fitness:", \
"%.3f" % round(self.ea.avg_fitness, 3), "\tStandard deviation: ", \
"%.5f" % round(self.ea.standard_deviation, 3), \
"Fitness components:", self.ea.phenotype_adult_pool[0].fitness_components, "\tTime left:", \
"%02d:%02d" % (divmod(self.timer * (self.horizontal_slider_12.get() - self.current_generation), 60)), \
"\tBest Phenotype:", self.ea.phenotype_adult_pool[0].components
self.fitness_log_average[self.current_run].append(self.ea.avg_fitness)
self.fitness_log_best[self.current_run].append(self.ea.phenotype_adult_pool[0].fitness_value)
self.standard_deviation_log[self.current_run].append(self.ea.standard_deviation)
class Gui(tk.Tk):
def __init__(self, delay, nr_of_runs=1, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs)
self.delay = delay
self.nr_of_runs = nr_of_runs
self.current_run = 0
self.current_generation = 0
self.fitness_log_average = []
self.fitness_log_best = []
self.standard_deviation_log = []
self.ea = None
self.build_parameter_menu()
def build_parameter_menu(self):
self.horizontal_slider_1 = tk.Scale(self, length=1000, from_=2, to=1000, orient=tk.HORIZONTAL,
label="Genotype Pool Size", resolution=2,
command=self.control_pool_size_genotype)
self.horizontal_slider_1.set(GENOTYPE_POOL_SIZE)
self.horizontal_slider_1.pack()
self.horizontal_slider_2 = tk.Scale(self, length=1000, from_=2, to=1000, orient=tk.HORIZONTAL,
label="Adult Pool Size", resolution=2, command=self.control_pool_size_adult)
self.horizontal_slider_2.set(ADULT_POOL_SIZE)
self.horizontal_slider_2.pack()
self.horizontal_slider_3 = tk.Scale(self, length=1000, from_=2, to=1000, orient=tk.HORIZONTAL,
label="Genotype Length")
self.horizontal_slider_3.set(GENOTYPE_LENGTH)
self.horizontal_slider_3.pack()
crossover_slider_group = tk.Frame(self)
self.horizontal_slider_4 = tk.Scale(crossover_slider_group, length=500, from_=0.0, to=1.0, resolution=-1,
orient=tk.HORIZONTAL, label="Crossover Rate")
self.horizontal_slider_4.set(CROSSOVER_RATE)
self.horizontal_slider_4.pack(side=tk.LEFT)
self.horizontal_slider_5 = tk.Scale(self, length=1000, from_=0, to=1, resolution=-1, orient=tk.HORIZONTAL,
label="Mutation Rate")
self.horizontal_slider_5.set(MUTATION_RATE)
self.horizontal_slider_5.pack()
self.horizontal_slider_7 = tk.Scale(crossover_slider_group, length=500, from_=1, to=100, orient=tk.HORIZONTAL,
label="Points of Crossover")
self.horizontal_slider_7.set(POINTS_OF_CROSSOVER)
self.horizontal_slider_7.pack(side=tk.LEFT)
crossover_slider_group.pack(anchor=tk.W)
self.horizontal_slider_8 = tk.Scale(self, length=1000, from_=0, to=300, orient=tk.HORIZONTAL,
label="Zero-threshold")
self.horizontal_slider_8.set(ZERO_THRESHOLD)
self.horizontal_slider_8.pack()
self.horizontal_slider_9 = tk.Scale(self, length=1000, from_=1, to=50, orient=tk.HORIZONTAL,
label="Symbol Set Size")
self.horizontal_slider_9.set(SYMBOL_SET_SIZE)
self.horizontal_slider_9.pack(anchor=tk.W)
tournament_slider_group = tk.Frame(self)
self.horizontal_slider_11 = tk.Scale(tournament_slider_group, length=500, from_=3, to=500, orient=tk.HORIZONTAL,
label="Tournament Size")
self.horizontal_slider_11.set(TOURNAMENT_SIZE)
self.horizontal_slider_11.pack(side=tk.LEFT)
self.horizontal_slider_12 = tk.Scale(tournament_slider_group, length=500, from_=0, to=1, resolution=-1, orient=tk.HORIZONTAL,
label="Tournament Slip-through Probability")
self.horizontal_slider_12.set(TOURNAMENT_SLIP_THROUGH_PROBABILITY)
self.horizontal_slider_12.pack(side=tk.LEFT)
tournament_slider_group.pack(anchor=tk.W)
self.horizontal_slider_13 = tk.Scale(self, length=1000, from_=1, to=1000, orient=tk.HORIZONTAL,
label="Initial Temperature")
self.horizontal_slider_13.set(INITIAL_TEMPERATURE)
self.horizontal_slider_13.pack()
self.horizontal_slider_14 = tk.Scale(self, length=1000, from_=1, to=10000, orient=tk.HORIZONTAL,
label="Max Number of Generations")
self.horizontal_slider_14.set(MAX_GENERATIONS)
self.horizontal_slider_14.pack()
self.mutation_protocol = tk.IntVar()
self.mutation_protocol.set(MUTATION_PROTOCOL)
radiogroup0 = tk.Frame(self)
tk.Label(radiogroup0, text="Mutation Protocol").pack(anchor=tk.W)
tk.Radiobutton(radiogroup0, text="Individual", variable=self.mutation_protocol, value=1).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup0, text="Component", variable=self.mutation_protocol, value=2).pack(side=tk.LEFT)
radiogroup0.pack(anchor=tk.W)
self.adult_selection_protocol = tk.IntVar()
self.adult_selection_protocol.set(ADULT_SELECTION_PROTOCOL)
radiogroup1 = tk.Frame(self)
tk.Label(radiogroup1, text="Adult Selection Protocol").pack(anchor=tk.W)
tk.Radiobutton(radiogroup1, text="Full", variable=self.adult_selection_protocol,
command=self.control_pool_size_adult, value=1).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup1, text="Over Production", variable=self.adult_selection_protocol,
command=self.control_pool_size_adult, value=2).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup1, text="Mixing", variable=self.adult_selection_protocol, value=3).pack(side=tk.LEFT)
radiogroup1.pack(anchor=tk.W)
self.parent_selection_protocol = tk.IntVar()
self.parent_selection_protocol.set(PARENT_SELECTION_PROTOCOL)
radiogroup2 = tk.Frame(self)
tk.Label(radiogroup2, text="Parent Selection Protocol").pack(anchor=tk.W)
tk.Radiobutton(radiogroup2, text="Fitness Proportionate", variable=self.parent_selection_protocol,
value=1).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup2, text="Sigma-scaling", variable=self.parent_selection_protocol,
value=2).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup2, text="Tournament selection", variable=self.parent_selection_protocol,
value=3).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup2, text="Boltzmann Selection", variable=self.parent_selection_protocol,
value=4).pack(side=tk.LEFT)
radiogroup2.pack(anchor=tk.W)
self.problem_type = tk.IntVar()
self.problem_type.set(PROBLEM)
radiogroup3 = tk.Frame(self)
tk.Label(radiogroup3, text="Problem type").pack(anchor=tk.W)
tk.Radiobutton(radiogroup3, text="One Max", variable=self.problem_type, value=1).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup3, text="LOLZ Prefix", variable=self.problem_type, value=2).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup3, text="Surprising Sequence Local", variable=self.problem_type,
value=3).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup3, text="Surprising Sequence Global", variable=self.problem_type,
value=4).pack(side=tk.LEFT)
radiogroup3.pack(anchor=tk.W)
start_button = tk.Button(self, text="Start", width=20, command=self.start_simulation)
start_button.pack()
def control_pool_size_genotype(self, event):
if self.adult_selection_protocol.get() == 1:
self.horizontal_slider_2.set(self.horizontal_slider_1.get())
elif self.adult_selection_protocol.get() == 2:
self.horizontal_slider_2.set(min(self.horizontal_slider_2.get(), self.horizontal_slider_1.get() - 2))
def control_pool_size_adult(self, event=None):
if self.adult_selection_protocol.get() == 1:
self.horizontal_slider_1.set(self.horizontal_slider_2.get())
elif self.adult_selection_protocol.get() == 2:
self.horizontal_slider_1.set(max(self.horizontal_slider_1.get(), self.horizontal_slider_2.get() - 6))
if self.parent_selection_protocol.get() == 3:
self.horizontal_slider_11.set(min(self.horizontal_slider_11.get(), self.horizontal_slider_2.get() // 2))
def start_simulation(self):
self.current_run = 0
self.fitness_log_average = []
self.fitness_log_best = []
self.standard_deviation_log = []
self.start_ea()
def start_ea(self):
self.ea = EvolutionaryAlgorithm(genotype_pool_size=self.horizontal_slider_1.get(),
adult_pool_size=self.horizontal_slider_2.get(),
genotype_length=self.horizontal_slider_3.get(),
phenotype_length=self.horizontal_slider_3.get(), # Not a single slider
adult_selection_protocol=self.adult_selection_protocol.get(),
parent_selection_protocol=self.parent_selection_protocol.get(),
crossover_rate=self.horizontal_slider_4.get(),
mutation_rate=self.horizontal_slider_5.get(),
mutation_protocol=self.mutation_protocol.get(),
points_of_crossover=self.horizontal_slider_7.get(),
zero_threshold=self.horizontal_slider_8.get(),
symbol_set_size=self.horizontal_slider_9.get(),
tournament_size=self.horizontal_slider_11.get(),
tournament_slip_through_probability=self.horizontal_slider_12.get(),
initial_temperature=self.horizontal_slider_13.get(),
problem=self.problem_type.get(),
generations=self.horizontal_slider_14.get())
self.current_generation = 0
self.fitness_log_average.append([])
self.fitness_log_best.append([])
self.standard_deviation_log.append([])
self.run_ea()
def run_ea(self):
self.ea.run_one_life_cycle()
self.write_to_log()
self.current_generation += 1
if self.current_generation < self.horizontal_slider_14.get() and \
self.ea.phenotype_adult_pool[0].fitness_value < 1.0:
self.after(self.delay, lambda: self.run_ea())
else:
print "End"
self.current_run += 1
if self.current_run < self.nr_of_runs:
self.start_ea()
else:
print "Average Generations:", sum([len(l) for l in self.fitness_log_average])/len(self.fitness_log_average)
self.plot_data()
def write_to_log(self):
print "Gen:", "%.2d" % self.current_generation, "\tBest fitness:", \
"%.3f" % round(self.ea.phenotype_adult_pool[0].fitness_value, 3), "\tAverage fitness:", \
"%.3f" % round(self.ea.avg_fitness, 3), "\tStandard deviation: ", \
"%.5f" % round(self.ea.standard_deviation, 3), "\tBest Phenotype:", \
self.ea.phenotype_adult_pool[0].components
if self.problem_type.get() > 2:
print "Violations: ", self.ea.phenotype_adult_pool[0].violations
self.fitness_log_average[self.current_run].append(self.ea.avg_fitness)
self.fitness_log_best[self.current_run].append(self.ea.phenotype_adult_pool[0].fitness_value)
self.standard_deviation_log[self.current_run].append(self.ea.standard_deviation)
def plot_data(self):
plt.figure(1)
plt.subplot(311)
plt.plot(self.fitness_log_average[-1], label="Average fitness")
plt.plot(self.fitness_log_best[-1], label="Best fitness")
#plt.legend(['y = Average fitness in adult pool', 'y = Best fitness in adult pool'], loc='lower right')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode="expand", borderaxespad=0.)
plt.subplot(312)
plt.plot(self.fitness_log_best[-1])
plt.legend(['y = Best fitness in adult pool'], loc='lower right')
plt.subplot(313)
plt.plot(self.standard_deviation_log[-1])
plt.legend(['y = Standard deviation'], loc='upper right')
plt.show()
class Gui(tk.Tk):
def __init__(self, delay, nr_of_runs=1, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs)
self.delay = delay
self.nr_of_runs = nr_of_runs
self.current_run = 0
self.current_generation = 0
self.fitness_log_average = []
self.fitness_log_best = []
self.standard_deviation_log = []
self.ea = None
self.build_parameter_menu()
def build_parameter_menu(self):
self.horizontal_slider_1 = tk.Scale(
self,
length=1000,
from_=2,
to=1000,
orient=tk.HORIZONTAL,
label="Genotype Pool Size",
resolution=2,
command=self.control_pool_size_genotype)
self.horizontal_slider_1.set(GENOTYPE_POOL_SIZE)
self.horizontal_slider_1.pack()
self.horizontal_slider_2 = tk.Scale(
self,
length=1000,
from_=2,
to=1000,
orient=tk.HORIZONTAL,
label="Adult Pool Size",
resolution=2,
command=self.control_pool_size_adult)
self.horizontal_slider_2.set(ADULT_POOL_SIZE)
self.horizontal_slider_2.pack()
self.horizontal_slider_3 = tk.Scale(self,
length=1000,
from_=2,
to=1000,
orient=tk.HORIZONTAL,
label="Genotype Length")
self.horizontal_slider_3.set(GENOTYPE_LENGTH)
self.horizontal_slider_3.pack()
crossover_slider_group = tk.Frame(self)
self.horizontal_slider_4 = tk.Scale(crossover_slider_group,
length=500,
from_=0.0,
to=1.0,
resolution=-1,
orient=tk.HORIZONTAL,
label="Crossover Rate")
self.horizontal_slider_4.set(CROSSOVER_RATE)
self.horizontal_slider_4.pack(side=tk.LEFT)
self.horizontal_slider_5 = tk.Scale(self,
length=1000,
from_=0,
to=1,
resolution=-1,
orient=tk.HORIZONTAL,
label="Mutation Rate")
self.horizontal_slider_5.set(MUTATION_RATE)
self.horizontal_slider_5.pack()
self.horizontal_slider_7 = tk.Scale(crossover_slider_group,
length=500,
from_=1,
to=100,
orient=tk.HORIZONTAL,
label="Points of Crossover")
self.horizontal_slider_7.set(POINTS_OF_CROSSOVER)
self.horizontal_slider_7.pack(side=tk.LEFT)
crossover_slider_group.pack(anchor=tk.W)
self.horizontal_slider_8 = tk.Scale(self,
length=1000,
from_=0,
to=300,
orient=tk.HORIZONTAL,
label="Zero-threshold")
self.horizontal_slider_8.set(ZERO_THRESHOLD)
self.horizontal_slider_8.pack()
self.horizontal_slider_9 = tk.Scale(self,
length=1000,
from_=1,
to=50,
orient=tk.HORIZONTAL,
label="Symbol Set Size")
self.horizontal_slider_9.set(SYMBOL_SET_SIZE)
self.horizontal_slider_9.pack(anchor=tk.W)
tournament_slider_group = tk.Frame(self)
self.horizontal_slider_11 = tk.Scale(tournament_slider_group,
length=500,
from_=3,
to=500,
orient=tk.HORIZONTAL,
label="Tournament Size")
self.horizontal_slider_11.set(TOURNAMENT_SIZE)
self.horizontal_slider_11.pack(side=tk.LEFT)
self.horizontal_slider_12 = tk.Scale(
tournament_slider_group,
length=500,
from_=0,
to=1,
resolution=-1,
orient=tk.HORIZONTAL,
label="Tournament Slip-through Probability")
self.horizontal_slider_12.set(TOURNAMENT_SLIP_THROUGH_PROBABILITY)
self.horizontal_slider_12.pack(side=tk.LEFT)
tournament_slider_group.pack(anchor=tk.W)
self.horizontal_slider_13 = tk.Scale(self,
length=1000,
from_=1,
to=1000,
orient=tk.HORIZONTAL,
label="Initial Temperature")
self.horizontal_slider_13.set(INITIAL_TEMPERATURE)
self.horizontal_slider_13.pack()
self.horizontal_slider_14 = tk.Scale(self,
length=1000,
from_=1,
to=10000,
orient=tk.HORIZONTAL,
label="Max Number of Generations")
self.horizontal_slider_14.set(MAX_GENERATIONS)
self.horizontal_slider_14.pack()
self.mutation_protocol = tk.IntVar()
self.mutation_protocol.set(MUTATION_PROTOCOL)
radiogroup0 = tk.Frame(self)
tk.Label(radiogroup0, text="Mutation Protocol").pack(anchor=tk.W)
tk.Radiobutton(radiogroup0,
text="Individual",
variable=self.mutation_protocol,
value=1).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup0,
text="Component",
variable=self.mutation_protocol,
value=2).pack(side=tk.LEFT)
radiogroup0.pack(anchor=tk.W)
self.adult_selection_protocol = tk.IntVar()
self.adult_selection_protocol.set(ADULT_SELECTION_PROTOCOL)
radiogroup1 = tk.Frame(self)
tk.Label(radiogroup1,
text="Adult Selection Protocol").pack(anchor=tk.W)
tk.Radiobutton(radiogroup1,
text="Full",
variable=self.adult_selection_protocol,
command=self.control_pool_size_adult,
value=1).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup1,
text="Over Production",
variable=self.adult_selection_protocol,
command=self.control_pool_size_adult,
value=2).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup1,
text="Mixing",
variable=self.adult_selection_protocol,
value=3).pack(side=tk.LEFT)
radiogroup1.pack(anchor=tk.W)
self.parent_selection_protocol = tk.IntVar()
self.parent_selection_protocol.set(PARENT_SELECTION_PROTOCOL)
radiogroup2 = tk.Frame(self)
tk.Label(radiogroup2,
text="Parent Selection Protocol").pack(anchor=tk.W)
tk.Radiobutton(radiogroup2,
text="Fitness Proportionate",
variable=self.parent_selection_protocol,
value=1).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup2,
text="Sigma-scaling",
variable=self.parent_selection_protocol,
value=2).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup2,
text="Tournament selection",
variable=self.parent_selection_protocol,
value=3).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup2,
text="Boltzmann Selection",
variable=self.parent_selection_protocol,
value=4).pack(side=tk.LEFT)
radiogroup2.pack(anchor=tk.W)
self.problem_type = tk.IntVar()
self.problem_type.set(PROBLEM)
radiogroup3 = tk.Frame(self)
tk.Label(radiogroup3, text="Problem type").pack(anchor=tk.W)
tk.Radiobutton(radiogroup3,
text="One Max",
variable=self.problem_type,
value=1).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup3,
text="LOLZ Prefix",
variable=self.problem_type,
value=2).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup3,
text="Surprising Sequence Local",
variable=self.problem_type,
value=3).pack(side=tk.LEFT)
tk.Radiobutton(radiogroup3,
text="Surprising Sequence Global",
variable=self.problem_type,
value=4).pack(side=tk.LEFT)
radiogroup3.pack(anchor=tk.W)
start_button = tk.Button(self,
text="Start",
width=20,
command=self.start_simulation)
start_button.pack()
def control_pool_size_genotype(self, event):
if self.adult_selection_protocol.get() == 1:
self.horizontal_slider_2.set(self.horizontal_slider_1.get())
elif self.adult_selection_protocol.get() == 2:
self.horizontal_slider_2.set(
min(self.horizontal_slider_2.get(),
self.horizontal_slider_1.get() - 2))
def control_pool_size_adult(self, event=None):
if self.adult_selection_protocol.get() == 1:
self.horizontal_slider_1.set(self.horizontal_slider_2.get())
elif self.adult_selection_protocol.get() == 2:
self.horizontal_slider_1.set(
max(self.horizontal_slider_1.get(),
self.horizontal_slider_2.get() - 6))
if self.parent_selection_protocol.get() == 3:
self.horizontal_slider_11.set(
min(self.horizontal_slider_11.get(),
self.horizontal_slider_2.get() // 2))
def start_simulation(self):
self.current_run = 0
self.fitness_log_average = []
self.fitness_log_best = []
self.standard_deviation_log = []
self.start_ea()
def start_ea(self):
self.ea = EvolutionaryAlgorithm(
genotype_pool_size=self.horizontal_slider_1.get(),
adult_pool_size=self.horizontal_slider_2.get(),
genotype_length=self.horizontal_slider_3.get(),
phenotype_length=self.horizontal_slider_3.get(
), # Not a single slider
adult_selection_protocol=self.adult_selection_protocol.get(),
parent_selection_protocol=self.parent_selection_protocol.get(),
crossover_rate=self.horizontal_slider_4.get(),
mutation_rate=self.horizontal_slider_5.get(),
mutation_protocol=self.mutation_protocol.get(),
points_of_crossover=self.horizontal_slider_7.get(),
zero_threshold=self.horizontal_slider_8.get(),
symbol_set_size=self.horizontal_slider_9.get(),
tournament_size=self.horizontal_slider_11.get(),
tournament_slip_through_probability=self.horizontal_slider_12.get(
),
initial_temperature=self.horizontal_slider_13.get(),
problem=self.problem_type.get(),
generations=self.horizontal_slider_14.get())
self.current_generation = 0
self.fitness_log_average.append([])
self.fitness_log_best.append([])
self.standard_deviation_log.append([])
self.run_ea()
def run_ea(self):
self.ea.run_one_life_cycle()
self.write_to_log()
self.current_generation += 1
if self.current_generation < self.horizontal_slider_14.get() and \
self.ea.phenotype_adult_pool[0].fitness_value < 1.0:
self.after(self.delay, lambda: self.run_ea())
else:
print "End"
self.current_run += 1
if self.current_run < self.nr_of_runs:
self.start_ea()
else:
print "Average Generations:", sum([
len(l) for l in self.fitness_log_average
]) / len(self.fitness_log_average)
self.plot_data()
def write_to_log(self):
print "Gen:", "%.2d" % self.current_generation, "\tBest fitness:", \
"%.3f" % round(self.ea.phenotype_adult_pool[0].fitness_value, 3), "\tAverage fitness:", \
"%.3f" % round(self.ea.avg_fitness, 3), "\tStandard deviation: ", \
"%.5f" % round(self.ea.standard_deviation, 3), "\tBest Phenotype:", \
self.ea.phenotype_adult_pool[0].components
if self.problem_type.get() > 2:
print "Violations: ", self.ea.phenotype_adult_pool[0].violations
self.fitness_log_average[self.current_run].append(self.ea.avg_fitness)
self.fitness_log_best[self.current_run].append(
self.ea.phenotype_adult_pool[0].fitness_value)
self.standard_deviation_log[self.current_run].append(
self.ea.standard_deviation)
def plot_data(self):
plt.figure(1)
plt.subplot(311)
plt.plot(self.fitness_log_average[-1], label="Average fitness")
plt.plot(self.fitness_log_best[-1], label="Best fitness")
#plt.legend(['y = Average fitness in adult pool', 'y = Best fitness in adult pool'], loc='lower right')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=2,
mode="expand",
borderaxespad=0.)
plt.subplot(312)
plt.plot(self.fitness_log_best[-1])
plt.legend(['y = Best fitness in adult pool'], loc='lower right')
plt.subplot(313)
plt.plot(self.standard_deviation_log[-1])
plt.legend(['y = Standard deviation'], loc='upper right')
plt.show()
