def __init__(self):
self._initial_population_size = 8
self._include_parents = True
self._birthrate = 2 # Number of children per parent chromosome
self._generation = -1 # First generation = 0, set in _new_generation(...)
self._chromosomes = []
self._fitness_archive = [
] # list(dict()) usage: fitness = self_fitness_archive[generation][chromosome]
self._fitness_dict = dict()
self._chromosome_counter = dict()
self._environment = DifficultEnvironment()
self._selector = NormalizedRatioSelector()
self._mutator = OneGeneChanceMutator(mutation_rate=0.2)
self._create_initial_population()
def __init__(self):
self._initial_population_size = 8
self._include_parents = True
self._birthrate = 2 # Number of children per parent chromosome
self._generation = -1 # First generation = 0, set in _new_generation(...)
self._chromosomes = []
self._fitness_archive = [] # list(dict()) usage: fitness = self_fitness_archive[generation][chromosome]
self._fitness_dict = dict()
self._chromosome_counter = dict()
self._environment = DifficultEnvironment()
self._selector = NormalizedRatioSelector()
self._mutator = OneGeneChanceMutator(mutation_rate=0.2)
self._create_initial_population()
class Evolution(object):
def __init__(self):
self._initial_population_size = 8
self._include_parents = True
self._birthrate = 2 # Number of children per parent chromosome
self._generation = -1 # First generation = 0, set in _new_generation(...)
self._chromosomes = []
self._fitness_archive = [
] # list(dict()) usage: fitness = self_fitness_archive[generation][chromosome]
self._fitness_dict = dict()
self._chromosome_counter = dict()
self._environment = DifficultEnvironment()
self._selector = NormalizedRatioSelector()
self._mutator = OneGeneChanceMutator(mutation_rate=0.2)
self._create_initial_population()
def current_generation(self):
return self._generation
def _check_fitness(self, chromosome):
try:
return self._fitness_dict[chromosome]
except KeyError:
self._fitness_dict[
chromosome] = self._environment.chromosome_fitness(chromosome)
return self._fitness_dict[chromosome]
def sorted_chromosomes(self):
ordered_dict = tuple(
(chromosome, self._fitness_dict[chromosome])
for chromosome, fitness in sorted(
self._fitness_dict.items(), key=itemgetter(1), reverse=True))
# keyvalue_pairs = list(ordered_dict.items())
return ordered_dict
def print_fitness(self):
print('{}Generation {}'.format(os.linesep, self._generation))
sorted_chromosomes = self.sorted_chromosomes()
try:
for chromosome, fitness in sorted_chromosomes:
print('Fitness: {:3f} - {}'.format(fitness, chromosome))
except ValueError as e:
print('error: {}'.format(e))
def tick(self):
self.evolve_next_generation()
self.print_fitness()
def evolve_next_generation(self):
selected = self._selector.select(self.sorted_chromosomes(),
k=self._initial_population_size / 2)
new_genes = self._crossover(selected)
mutated_genes = [self._mutator.mutate(genes) for genes in new_genes]
next_generation = mutated_genes
if self._include_parents:
next_generation.extend([parent.genes() for parent in selected])
self._new_generation_from_genes(next_generation)
def best_chromosome(self):
sorteds = self.sorted_chromosomes()
try:
return sorteds[0][0]
except (IndexError, KeyError):
return None
def best_fitness(self):
sorteds = self.sorted_chromosomes()
try:
return sorteds[0][1]
except (IndexError, KeyError):
return None
def avg_fitness(self):
try:
sz = len(self._chromosomes)
return sum(fitness
for (_, fitness) in self._fitness_dict.items()) / sz
except TypeError as e:
print('Error: {}'.format(e))
def _new_generation_from_genes(self, list_of_genes):
self._new_generation(
(Chromosome(genes=genes) for genes in list_of_genes))
def _new_generation(self, chromosomes):
self._chromosomes = []
if self._generation >= 0:
# Save existing generation in archive
self._fitness_archive.append(self._fitness_dict)
self._fitness_dict = dict()
self._generation += 1
for chromosome in chromosomes:
self._add_chromosome(chromosome)
def _create_initial_population(self):
# Will create random chromosome by default
chromosomes = [
Chromosome() for _ in range(self._initial_population_size)
]
self._new_generation(chromosomes)
self.print_fitness()
def _add_chromosome(self, chromosome):
self._count_chromosome(chromosome)
self._chromosomes.append(chromosome)
self._check_fitness(chromosome)
def _count_chromosome(self, chromosome):
try:
self._chromosome_counter[tuple(chromosome.genes())] += 1
except KeyError:
self._chromosome_counter[tuple(chromosome.genes())] = 1
def _crossover(self, parent_chromosomes):
parent_as = [
parent for i, parent in enumerate(parent_chromosomes) if i % 2
]
parent_bs = [
parent for parent in parent_chromosomes if parent not in parent_as
]
new_genes = []
offspring_per_parent = self._birthrate - (1 if self._include_parents
else 0)
for i, parent_a in enumerate(parent_as):
parent_b = parent_bs[i]
for _ in range(offspring_per_parent):
new_genes.extend(self._random_crossover(parent_a, parent_b))
return new_genes
def _random_crossover(self, chromosome_a, chromosome_b, n_cross_points=2):
cross_points = seeded_random.sample(range(chromosome_a.length()),
n_cross_points)
cross_points.sort()
return self._crossover_from_points(chromosome_a, chromosome_b,
cross_points)
def _crossover_from_points(self, chromosome_a, chromosome_b, cross_points):
genes_a = []
genes_b = []
a = chromosome_a.genes()
b = chromosome_b.genes()
n_cross = len(cross_points)
n_genes = len(a)
previous_i = 0
for i, cross_point in enumerate(cross_points):
genes_a.extend(a[previous_i:cross_point])
genes_b.extend(b[previous_i:cross_point])
tmp = a
a = b
b = tmp
previous_i = cross_point
if previous_i < n_genes:
genes_a += a[previous_i:]
genes_b += b[previous_i:]
return genes_a, genes_b
class Evolution(object):
def __init__(self):
self._initial_population_size = 8
self._include_parents = True
self._birthrate = 2 # Number of children per parent chromosome
self._generation = -1 # First generation = 0, set in _new_generation(...)
self._chromosomes = []
self._fitness_archive = [] # list(dict()) usage: fitness = self_fitness_archive[generation][chromosome]
self._fitness_dict = dict()
self._chromosome_counter = dict()
self._environment = DifficultEnvironment()
self._selector = NormalizedRatioSelector()
self._mutator = OneGeneChanceMutator(mutation_rate=0.2)
self._create_initial_population()
def current_generation(self):
return self._generation
def _check_fitness(self, chromosome):
try:
return self._fitness_dict[chromosome]
except KeyError:
self._fitness_dict[chromosome] = self._environment.chromosome_fitness(chromosome)
return self._fitness_dict[chromosome]
def sorted_chromosomes(self):
ordered_dict = tuple((chromosome, self._fitness_dict[chromosome])
for chromosome, fitness in
sorted(self._fitness_dict.items(), key=itemgetter(1), reverse=True))
# keyvalue_pairs = list(ordered_dict.items())
return ordered_dict
def print_fitness(self):
print('{}Generation {}'.format(os.linesep, self._generation))
sorted_chromosomes = self.sorted_chromosomes()
try:
for chromosome, fitness in sorted_chromosomes:
print('Fitness: {:3f} - {}'.format(fitness, chromosome))
except ValueError as e:
print('error: {}'.format(e))
def tick(self):
self.evolve_next_generation()
self.print_fitness()
def evolve_next_generation(self):
selected = self._selector.select(self.sorted_chromosomes(), k=self._initial_population_size / 2)
new_genes = self._crossover(selected)
mutated_genes = [self._mutator.mutate(genes) for genes in new_genes]
next_generation = mutated_genes
if self._include_parents:
next_generation.extend([parent.genes() for parent in selected])
self._new_generation_from_genes(next_generation)
def best_chromosome(self):
sorteds = self.sorted_chromosomes()
try:
return sorteds[0][0]
except (IndexError, KeyError):
return None
def best_fitness(self):
sorteds = self.sorted_chromosomes()
try:
return sorteds[0][1]
except (IndexError, KeyError):
return None
def avg_fitness(self):
try:
sz = len(self._chromosomes)
return sum(fitness for (_, fitness) in self._fitness_dict.items())/sz
except TypeError as e:
print('Error: {}'.format(e))
def _new_generation_from_genes(self, list_of_genes):
self._new_generation((Chromosome(genes=genes) for genes in list_of_genes))
def _new_generation(self, chromosomes):
self._chromosomes = []
if self._generation >= 0:
# Save existing generation in archive
self._fitness_archive.append(self._fitness_dict)
self._fitness_dict = dict()
self._generation += 1
for chromosome in chromosomes:
self._add_chromosome(chromosome)
def _create_initial_population(self):
# Will create random chromosome by default
chromosomes = [Chromosome() for _ in range(self._initial_population_size)]
self._new_generation(chromosomes)
self.print_fitness()
def _add_chromosome(self, chromosome):
self._count_chromosome(chromosome)
self._chromosomes.append(chromosome)
self._check_fitness(chromosome)
def _count_chromosome(self, chromosome):
try:
self._chromosome_counter[tuple(chromosome.genes())] += 1
except KeyError:
self._chromosome_counter[tuple(chromosome.genes())] = 1
def _crossover(self, parent_chromosomes):
parent_as = [parent for i, parent in enumerate(parent_chromosomes) if i % 2]
parent_bs = [parent for parent in parent_chromosomes if parent not in parent_as]
new_genes = []
offspring_per_parent = self._birthrate - (1 if self._include_parents else 0)
for i, parent_a in enumerate(parent_as):
parent_b = parent_bs[i]
for _ in range(offspring_per_parent):
new_genes.extend(self._random_crossover(parent_a, parent_b))
return new_genes
def _random_crossover(self, chromosome_a, chromosome_b, n_cross_points=2):
cross_points = seeded_random.sample(range(chromosome_a.length()), n_cross_points)
cross_points.sort()
return self._crossover_from_points(chromosome_a, chromosome_b, cross_points)
def _crossover_from_points(self, chromosome_a, chromosome_b, cross_points):
genes_a = []
genes_b = []
a = chromosome_a.genes()
b = chromosome_b.genes()
n_cross = len(cross_points)
n_genes = len(a)
previous_i = 0
for i, cross_point in enumerate(cross_points):
genes_a.extend(a[previous_i:cross_point])
genes_b.extend(b[previous_i:cross_point])
tmp = a
a = b
b = tmp
previous_i = cross_point
if previous_i < n_genes:
genes_a += a[previous_i:]
genes_b += b[previous_i:]
return genes_a, genes_b