fitness = -sys.maxint
else:
new_stdout.seek(0)
output = new_stdout.read()
print output
for (output_char, target_char) in zip(output, TARGET_PROGRAM_OUTPUT):
fitness += character_fitness(output_char, target_char)
return fitness
def breed_programs(prog1, prog2):
return breed_strings(prog1, prog2, WEIGHTED_COMMANDS, MUTATION_RATE)
def stop_condition(candidate):
if candidate.fitness == len(TARGET_PROGRAM_OUTPUT):
return True
else:
return False
if __name__ == "__main__":
ga.run_genetic_algorithm(spawn_func=generate_random_program,
breed_func=breed_programs,
fitness_func=calculate_fitness,
stop_condition=stop_condition,
population_size=POPULATION_SIZE)
return child
def calc_fitness(func):
try:
x_vals = xrange(-100, 100, 1)
reference_vals = map(target, x_vals)
tested_vals = map(func, x_vals)
differences = [(r - t) for (r, t) in zip(reference_vals, tested_vals)]
sum_of_squares = sum([a*a for a in differences])
return -sum_of_squares
except ZeroDivisionError:
return -sys.maxint
def stop_condition(candidate):
if candidate.fitness == 0:
return True
else:
return False
if __name__ == "__main__":
ga.run_genetic_algorithm(spawn_func=generate_tree,
breed_func=breed,
fitness_func=calc_fitness,
stop_condition=stop_condition,
population_size=POPULATION_SIZE)
return generate_random_string(CHARACTERS, MAX_STRING_LENGTH)
def calculate_fitness(dna):
fitness = 0
for (a, b) in zip(dna, TARGET):
if a == b:
fitness += 1
diff_length = abs(len(dna) - len(TARGET))
fitness -= (diff_length*1.1)
return fitness
def crossover(string1, string2):
return breed_strings(string1, string2, CHARACTERS, MUTATION_RATE)
def stop_condition(candidate):
if candidate.this == TARGET:
return True
else:
return False
if __name__ == "__main__":
ga.run_genetic_algorithm(spawn_func=generate_candidate,
breed_func=crossover,
fitness_func=calculate_fitness,
stop_condition=stop_condition,
population_size=POPULATION_SIZE)
fitness = -sys.maxint
else:
new_stdout.seek(0)
output = new_stdout.read()
print output
for (output_char, target_char) in zip(output, TARGET_PROGRAM_OUTPUT):
fitness += character_fitness(output_char, target_char)
return fitness
def breed_programs(prog1, prog2):
return breed_strings(prog1, prog2, WEIGHTED_COMMANDS, MUTATION_RATE)
def stop_condition(candidate):
if candidate.fitness == len(TARGET_PROGRAM_OUTPUT):
return True
else:
return False
if __name__ == "__main__":
ga.run_genetic_algorithm(spawn_func=generate_random_program,
breed_func=breed_programs,
fitness_func=calculate_fitness,
stop_condition=stop_condition,
population_size=POPULATION_SIZE)
return child
def calc_fitness(func):
try:
x_vals = xrange(-100, 100, 1)
reference_vals = map(target, x_vals)
tested_vals = map(func, x_vals)
differences = [(r - t) for (r, t) in zip(reference_vals, tested_vals)]
sum_of_squares = sum([a * a for a in differences])
return -sum_of_squares
except ZeroDivisionError:
return -sys.maxint
def stop_condition(candidate):
if candidate.fitness == 0:
return True
else:
return False
if __name__ == "__main__":
ga.run_genetic_algorithm(spawn_func=generate_tree,
breed_func=breed,
fitness_func=calc_fitness,
stop_condition=stop_condition,
population_size=POPULATION_SIZE)
