def setUp(self):
self.genome1 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome2 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome3 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome4 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome5 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome6 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome7 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome1.fitness = 2
self.genome2.fitness = 0
self.genome3.fitness = 22
self.genome4.fitness = 13
self.genome5.fitness = 2
self.genome6.fitness = 6
self.genome7.fitness = 8
self.group = Group()
self.group.add_genome(self.genome1)
self.group.add_genome(self.genome2)
self.group.add_genome(self.genome3)
self.group.add_genome(self.genome4)
self.group.add_genome(self.genome5)
self.group.add_genome(self.genome6)
self.group.add_genome(self.genome7)
self.group.adjust_genomes_fitness()
self.group.calculate_group_adjusted_fitness_score()
def setUp(self):
Group._GROUP_ID = 0
Generation._GENERATION_ID = 0
self.genome1 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome2 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome3 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome4 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome5 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome6 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome7 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome1.fitness = 2
self.genome2.fitness = 0
self.genome3.fitness = 22
self.genome4.fitness = 13
self.genome5.fitness = 2
self.genome6.fitness = 6
self.genome7.fitness = 8
self.group = Group()
self.group.add_genome(self.genome1)
self.group.add_genome(self.genome2)
self.group.add_genome(self.genome3)
self.group.add_genome(self.genome4)
self.group.add_genome(self.genome5)
self.group.add_genome(self.genome6)
self.group.add_genome(self.genome7)
self.logger = Logger()
self.generation = Generation([self.group], logger=self.logger)
def test_genomes_fitness_score_adjusting(self):
genome1 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome2 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome3 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome4 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome5 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome6 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
group1 = Group()
group2 = Group()
group1.add_genome(genome1)
group1.add_genome(genome2)
group1.add_genome(genome3)
group1.add_genome(genome4)
group2.add_genome(genome5)
group2.add_genome(genome6)
generation = Generation([group1, group2])
generation.create_phenotypes()
generation.run_phenotypes()
generation.update_genomes_fitness_scores(
generation.get_phenotypes_fitness_scores())
generation.adjust_genomes_fitness_scores()
self.assertEqual(genome1.adjusted_fitness, 0.25)
self.assertAlmostEqual(genome5.adjusted_fitness, 0.5)
class TestLoggerCase(unittest.TestCase):
def setUp(self):
Group._GROUP_ID = 0
Generation._GENERATION_ID = 0
self.genome1 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome2 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome3 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome4 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome5 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome6 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome7 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome1.fitness = 2
self.genome2.fitness = 0
self.genome3.fitness = 22
self.genome4.fitness = 13
self.genome5.fitness = 2
self.genome6.fitness = 6
self.genome7.fitness = 8
self.group = Group()
self.group.add_genome(self.genome1)
self.group.add_genome(self.genome2)
self.group.add_genome(self.genome3)
self.group.add_genome(self.genome4)
self.group.add_genome(self.genome5)
self.group.add_genome(self.genome6)
self.group.add_genome(self.genome7)
self.logger = Logger()
self.generation = Generation([self.group], logger=self.logger)
def test_logging(self):
self.assertIs(self.logger.log[0].groups_log[0], self.group)
def test_evolve_xor(self):
print("testing advanced xor")
Group._GROUP_ID = 0
Generation._GENERATION_ID = 0
specie = Group()
c1 = ConnectionGene(1, 3, enabled=True)
c2 = ConnectionGene(2, 3, enabled=True)
c3 = ConnectionGene(2, 3, enabled=True)
c4 = ConnectionGene(2, 3, enabled=True)
c5 = ConnectionGene(2, 3, enabled=True)
c6 = ConnectionGene(2, 3, enabled=True)
c7 = ConnectionGene(2, 3, enabled=True)
c8 = ConnectionGene(2, 3, enabled=True)
c9 = ConnectionGene(2, 3, enabled=True)
c10 = ConnectionGene(2, 3, enabled=True)
c11 = ConnectionGene(2, 3, enabled=True)
c12 = ConnectionGene(2, 3, enabled=True)
c13 = ConnectionGene(2, 3, enabled=True)
c14 = ConnectionGene(2, 3, enabled=True)
c15 = ConnectionGene(2, 3, enabled=True)
c16 = ConnectionGene(2, 3, enabled=True)
c17 = ConnectionGene(2, 3, enabled=True)
c18 = ConnectionGene(2, 3, enabled=True)
c19 = ConnectionGene(2, 3, enabled=True)
c20 = ConnectionGene(2, 3, enabled=True)
c21 = ConnectionGene(2, 3, enabled=True)
c22 = ConnectionGene(2, 3, enabled=True)
c23 = ConnectionGene(2, 3, enabled=True)
c24 = ConnectionGene(2, 3, enabled=True)
c25 = ConnectionGene(2, 3, enabled=True)
c26 = ConnectionGene(2, 3, enabled=True)
c27 = ConnectionGene(2, 3, enabled=True)
c28 = ConnectionGene(2, 3, enabled=True)
c29 = ConnectionGene(2, 3, enabled=True)
c30 = ConnectionGene(2, 3, enabled=True)
c31 = ConnectionGene(2, 3, enabled=True)
c31 = ConnectionGene(2, 3, enabled=True)
c33 = ConnectionGene(2, 3, enabled=True)
for i in range(10):
specie.add_genome(
Genome([
[1, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[1, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[1, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[1, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[2, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[2, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[2, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[2, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[3, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[3, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[3, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[3, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[4, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[4, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[4, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[4, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[5, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[5, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[5, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[5, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[6, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[6, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[6, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[6, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[7, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[7, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[7, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[7, 12,
random.normalvariate(mu=0.0, sigma=1.0), True],
[8, 9, random.normalvariate(mu=0.0, sigma=1.0), True],
[8, 10,
random.normalvariate(mu=0.0, sigma=1.0), True],
[8, 11,
random.normalvariate(mu=0.0, sigma=1.0), True],
[8, 12,
random.normalvariate(mu=0.0, sigma=1.0), True]
], 8, 4))
mutation_coefficients = {
'add_connection': 0.5,
'split_connection': 0.2,
'change_weight': 0.8,
'new_connection_abs_max_weight': 1.0,
'max_weight_mutation': 0.25
}
compatibility_coefficients = {
'excess_factor': 1.0,
'disjoint_factor': 1.0,
'weight_difference_factor': 0.5
}
log = Logger()
gen = Generation([specie],
mutation_coefficients=mutation_coefficients,
compatibility_coefficients=compatibility_coefficients,
logger=log)
i = 1
while i < 8:
print(i)
gen = gen.create_new_generation()
i += 1
best_nn = NeuralNetwork(Generation.best_genome)
print(best_nn.forward([0, 1, 1, 0, 0, 1, 1, 0]))
print(str(best_nn._genome.fitness) + "/" + str(256 * 4))
def test_simple_xor(self):
print("testing simple xor")
Group._GROUP_ID = 0
Generation._GENERATION_ID = 0
specie = Group()
for i in range(16):
for j in range(17, 21):
ConnectionGene(i + 1, j, enabled=True)
connection_list = []
z = 0
for i in range(16):
for j in range(17, 21):
connection_list.append([
i + 1, j,
random.normalvariate(mu=0.0, sigma=1.0), True, z
])
z += 1
print(connection_list)
for i in range(10):
specie.add_genome(
Genome(
[[1, 3,
random.normalvariate(mu=0.0, sigma=1.0), True, 0],
[2, 3,
random.normalvariate(mu=0.0, sigma=1.0), True, 1]], 2,
1))
mutation_coefficients = {
'add_connection': 0.5,
'split_connection': 0.2,
'change_weight': 0.8,
'new_connection_abs_max_weight': 1.0,
'max_weight_mutation': 0.5
}
compatibility_coefficients = {
'excess_factor': 1.0,
'disjoint_factor': 1.0,
'weight_difference_factor': 2.0
}
log = Logger()
gen = Generation([specie],
mutation_coefficients=mutation_coefficients,
compatibility_coefficients=compatibility_coefficients,
logger=log)
i = 1
while i < 150:
print(i)
gen = gen.create_new_generation()
i += 1
best_nn = NeuralNetwork(Generation.best_genome)
a = (best_nn.forward([0.0, 0.0]))
b = (best_nn.forward([0.0, 1.0]))
c = (best_nn.forward([1.0, 0.0]))
d = (best_nn.forward([1.0, 1.0]))
print(a)
print(b)
print(c)
print(d)
print(4.0 - (a[0] - 0)**2 - (b[0] - 1)**2 - (c[0] - 1)**2 -
(d[0] - 0)**2)
print(best_nn._genome.fitness)
groups_count = []
for generation_log in gen.logger.log.values():
groups_count.append(len(generation_log.groups_log))
plt.plot(list(gen.logger.log.keys()), groups_count)
plt.xlabel("Generation")
plt.ylabel("Number of groups")
plt.title("Groups amount change over evolution")
plt.savefig("plot of gen count")
plt.clf()
last_gen_groups_fitness = []
for fit in gen.logger.log[gen.id -
1].groups_fitness_scores_log.values():
last_gen_groups_fitness.append(fit[0][2])
plt.plot(list(gen.logger.log[gen.id - 1].groups_log.keys()),
last_gen_groups_fitness, 'ro')
plt.xlabel("Group")
plt.ylabel("Group adjusted fitness")
plt.title("Adjusted fitness of groups in last generation")
plt.savefig("plot of last gen fitness")
plt.clf()
plt.plot(list(Generation.best_fitnesses.keys()),
list(Generation.best_fitnesses.values()))
plt.xlabel("Generation")
plt.ylabel("Fitness score")
plt.title("Fitness score progression")
plt.savefig("plot of fitness")
def test_making_new_generation(self):
Group._GROUP_ID = 0
Generation._GENERATION_ID = 0
genome1 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome2 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome3 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome4 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome5 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome6 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome7 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
group1 = Group()
group2 = Group()
group1.add_genome(genome1)
group1.add_genome(genome2)
group1.add_genome(genome3)
group1.add_genome(genome4)
group2.add_genome(genome5)
group2.add_genome(genome6)
group2.add_genome(genome7)
generation = Generation([group1, group2])
# for i in range(100):
# print(i)
# generation = generation.create_new_generation()
print("Done")
def test_calculating_offsprings(self):
Group._GROUP_ID = 0
Generation._GENERATION_ID = 0
genome1 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome2 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome3 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome4 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome5 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome6 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome7 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
group1 = Group()
group2 = Group()
group1.add_genome(genome1)
group1.add_genome(genome2)
group1.add_genome(genome3)
group1.add_genome(genome4)
group2.add_genome(genome5)
group2.add_genome(genome6)
group2.add_genome(genome7)
generation = Generation([group1, group2])
generation.create_phenotypes()
generation.run_phenotypes()
generation.update_genomes_fitness_scores(
generation.get_phenotypes_fitness_scores())
generation.adjust_genomes_fitness_scores()
a = generation.calculate_groups_adjusted_fitness_scores()
offspring_count = generation.calculate_groups_offsprings(
a, sum(a.values()))
self.assertEqual(offspring_count[0], 50)
self.assertEqual(offspring_count[1], 50)
genome1.adjusted_fitness = 1
a = generation.calculate_groups_adjusted_fitness_scores()
offspring_count = generation.calculate_groups_offsprings(
a, sum(a.values()))
self.assertEqual(offspring_count[0], 64)
self.assertEqual(offspring_count[1], 36)
class TestGroupCase(unittest.TestCase):
def setUp(self):
self.genome1 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome2 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome3 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome4 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome5 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome6 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome7 = Genome([[1, 3, 0, True], [1, 4, 0, True],
[2, 3, 0, True], [2, 4, 0, True]], 2, 1)
self.genome1.fitness = 2
self.genome2.fitness = 0
self.genome3.fitness = 22
self.genome4.fitness = 13
self.genome5.fitness = 2
self.genome6.fitness = 6
self.genome7.fitness = 8
self.group = Group()
self.group.add_genome(self.genome1)
self.group.add_genome(self.genome2)
self.group.add_genome(self.genome3)
self.group.add_genome(self.genome4)
self.group.add_genome(self.genome5)
self.group.add_genome(self.genome6)
self.group.add_genome(self.genome7)
self.group.adjust_genomes_fitness()
self.group.calculate_group_adjusted_fitness_score()
def test_adjust_genome_fitnesses(self):
self.assertAlmostEqual(self.genome1.adjusted_fitness, 0.28571428, 5)
self.assertAlmostEqual(self.genome2.adjusted_fitness, 0)
self.assertAlmostEqual(self.genome3.adjusted_fitness, 3.14285714, 5)
self.assertAlmostEqual(self.genome4.adjusted_fitness, 1.8571428, 5)
self.assertAlmostEqual(self.genome5.adjusted_fitness, 0.28571428, 5)
self.assertAlmostEqual(self.genome6.adjusted_fitness, 0.85714285, 5)
self.assertAlmostEqual(self.genome7.adjusted_fitness, 1.14285714, 5)
self.assertAlmostEqual(
self.group.group_adjusted_fitness,
sum([
self.genome1.adjusted_fitness, self.genome2.adjusted_fitness,
self.genome3.adjusted_fitness, self.genome4.adjusted_fitness,
self.genome5.adjusted_fitness, self.genome6.adjusted_fitness,
self.genome7.adjusted_fitness
]))
def test_get_representative(self):
for i in range(1000000):
gen = self.group.get_representative()
def test_ceil(self):
self.assertEqual(math.ceil(0.1 * 1), 1)
self.assertEqual(math.ceil(0.1 * 2), 1)
self.assertEqual(math.ceil(0.1 * 3), 1)
self.assertEqual(math.ceil(0.5 * 1), 1)
self.assertEqual(math.ceil(0.5 * 3), 2)
self.assertEqual(math.ceil(0.1 * 1), 1)
self.assertEqual(math.ceil(0.9 * 12), 11)
self.assertEqual(math.ceil(0.1 * 0), 0)
self.assertEqual(math.ceil(0 * 11), 0)
def test_get_parents(self):
parents = self.group.get_parents(0.3)
self.assertIs(parents[0], self.genome3)
self.assertIs(parents[1], self.genome4)
self.assertIs(parents[2], self.genome7)
def test_create_phenotypes(self):
generation = Generation()
generation.groups[0] = self.group
generation.create_phenotypes()
first_phenotype = generation.phenotypes[0]
self.assertEqual(1 in first_phenotype._neurons, True)
self.assertEqual(2 in first_phenotype._neurons, True)
self.assertEqual(3 in first_phenotype._neurons, True)
self.assertEqual(4 in first_phenotype._neurons, True)
self.assertEqual(5 in first_phenotype._neurons, False)
self.assertEqual(1 in first_phenotype._input_neurons, True)
self.assertEqual(2 in first_phenotype._input_neurons, True)
self.assertEqual(3 in first_phenotype._input_neurons, False)
self.assertEqual(4 in first_phenotype._input_neurons, False)
self.assertEqual(5 in first_phenotype._input_neurons, False)
self.assertEqual(1 in first_phenotype._output_neurons, False)
self.assertEqual(2 in first_phenotype._output_neurons, False)
self.assertEqual(3 in first_phenotype._output_neurons, True)
self.assertEqual(4 in first_phenotype._output_neurons, False)
self.assertEqual(5 in first_phenotype._output_neurons, False)
self.assertIs(first_phenotype._genome, self.genome1)
def test_phenotypes_run(self):
generation = Generation([self.group])
generation.create_phenotypes()
generation.run_phenotypes()
losses = generation.get_phenotypes_fitness_scores()
self.assertEqual(losses[0], 1)
self.assertEqual(losses[6], 1)
def test_genomes_fitness_score_adjusting(self):
genome1 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome2 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome3 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome4 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome5 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
genome6 = Genome([[1, 3, 0, True], [1, 4, 0, True], [2, 3, 0, True],
[2, 4, 0, True]], 2, 1)
group1 = Group()
group2 = Group()
group1.add_genome(genome1)
group1.add_genome(genome2)
group1.add_genome(genome3)
group1.add_genome(genome4)
group2.add_genome(genome5)
group2.add_genome(genome6)
generation = Generation([group1, group2])
generation.create_phenotypes()
generation.run_phenotypes()
generation.update_genomes_fitness_scores(
generation.get_phenotypes_fitness_scores())
generation.adjust_genomes_fitness_scores()
self.assertEqual(genome1.adjusted_fitness, 0.25)
self.assertAlmostEqual(genome5.adjusted_fitness, 0.5)