def get_fittest_individual(iterable=set):
max_fitness = iterable[0]
for c in iterable:
if queen_lib.fitness_fn_positive(
max_fitness) < queen_lib.fitness_fn_positive(c):
max_fitness = c
return max_fitness
def random_selection(population):
"""
Compute fitness of each in population according to fitness_fn and add up
the total. Then choose 2 from sequence based on percentage contribution to
total fitness of population
Return selected variable which holds two individuals that were chosen as
the mother and the father
"""
# Python sets are randomly ordered. Since we traverse the set twice, we
# want to do it in the same order. So let's convert it temporarily to a
# list.
wheel = []
total_fitness = 0
ordered_population = list(population)
for i in ordered_population:
total_fitness = queen_lib.fitness_fn_positive(i)
for j in range(total_fitness):
wheel.append(i)
lenght = len(wheel) - 1
mother = wheel[random.randint(0, lenght)]
father = wheel[random.randint(0, lenght)]
return mother, father
def genetic_algorithm(population):
generation = 0
fittest_individual = get_fittest_individual(population)
bool = True
while queen_lib.fitness_fn_positive(fittest_individual) < minimal_fitness:
if bool:
print("Generation {}:".format(generation))
print_population(population)
print('Fittest Individual: ',
str(queen_lib.fitness_fn_positive(fittest_individual)))
new_population = []
for i in range(len(population)):
mother, father = random_selection(population)
child = reproduce(mother, father)
if random.uniform(0, 1) < p_mutation:
child = mutate(child)
new_population.append(child)
fittest_individual = get_fittest_individual(population)
bool = False
for i in range(len(population)):
if queen_lib.fitness_fn_positive(
population[i]) < queen_lib.fitness_fn_positive(
new_population[i]):
population[i] = new_population[i]
bool = True
generation += 1
print("Final generation {}:".format(generation))
print_population(population)
return fittest_individual
def getTotalFitness(population):
total_fitness = 0
for c in population:
total_fitness += queen_lib.fitness_fn_positive(c)
return total_fitness
def print_population(population):
for individual in population:
fitness = queen_lib.fitness_fn_positive(individual)
print("{} - fitness: {}".format(individual, fitness))