def test_default():
# Create the Genetic algorithm
ga = GA()
# Evolve the genetic algorithm
ga.evolve()
# Print your default genetic algorithm
ga.print_generation()
ga.print_population()
def test_while_ga_active():
# Create the Genetic algorithm
ga = GA()
# Set necessary attributes
ga.generation_goal = 1
# Evolve using ga.active
while ga.active():
ga.evolve(5)
def test_termination_impl():
# Create the Genetic algorithm
ga = GA()
# Set the termination_impl
ga.termination_impl = Termination.fitness_and_generation_based
# Evolve the genetic algorithm
ga.evolve()
assert (ga.termination_impl == Termination.fitness_and_generation_based) and (ga != None)
def test_survivor_selection_impl():
# Create the Genetic algorithm
ga = GA()
# Set the survivor_selection_impl
ga.survivor_selection_impl = Survivor.fill_in_random
# Evolve the genetic algorithm
ga.evolve()
assert (ga.survivor_selection_impl == Survivor.fill_in_random) and (ga != None)
def test_mutation_individual_impl():
# Create the Genetic algorithm
ga = GA()
# Set the mutation_population_impl
ga.mutation_individual_impl = Mutation.Individual.single_gene
# Evolve the genetic algorithm
ga.evolve()
assert (ga.mutation_individual_impl == Mutation.Individual.single_gene) and (ga != None)
def test_mutation_population_impl():
# Create the Genetic algorithm
ga = GA()
# Set the mutation_population_impl
ga.mutation_population_impl = Mutation.Population.random_selection
# Evolve the genetic algorithm
ga.evolve()
assert (ga.mutation_population_impl == Mutation.Population.random_selection) and (ga != None)
def test_crossover_individual_impl():
# Create the Genetic algorithm
ga = GA()
# Set the crossover_individual_impl
ga.crossover_individual_impl = Crossover.Individual.single_point
# Evolve the genetic algorithm
ga.evolve()
assert (ga.crossover_individual_impl == Crossover.Individual.single_point) and (ga != None)
def test_crossover_population_impl():
# Create the Genetic algorithm
ga = GA()
# Set the crossover_population_impl
ga.crossover_population_impl = Cossover.Population.sequential_selection
# Evolve the genetic algorithm
ga.evolve()
assert (ga.crossover_population_impl == Crossover.Population.sequential_selection) and (ga != None)
def test_parent_selection_impl():
# Create the Genetic algorithm
ga = GA()
# Set the parent_selection_impl
ga.parent_selection_impl = Parent.Fitness.roulette
# Evolve the genetic algorithm
ga.evolve()
assert (ga.parent_selection_impl == Parent.Fitness.roulette) and (ga != None)
def test_attributes_gene_impl():
# Create the Genetic algorithm
ga = GA()
# Set necessary attributes
ga.population_size = 3
ga.chromosome_length = 5
ga.generation_goal = 1
# Set gene_impl
ga.gene_impl = lambda: random.randint(1, 10)
# Evolve the genetic algorithm
ga.evolve()
def test_population_size():
"""Test the population size is create correctly"""
for i in range(4,100):
# Create the ga to test
ga = GA()
ga.generation_goal = 10
# Set the upper limit of testing
ga.population_size = i
# Evolve the ga
ga.evolve()
# If they are not equal throw an error
assert int(len(ga.population)) == ga.population_size
def test_chromosome_length():
""" Test to see if the actual chromosome length is the same as defined."""
# Test from 0 to 100 chromosome length
for i in range(1,100):
# Create the ga to test
ga = GA()
ga.generation_goal = 10
# Set the upper limit of testing
ga.chromosome_length = i
# Evolve the ga
ga.evolve()
# If they are not equal throw an error
assert len(ga.population.chromosome_list[0]) == ga.chromosome_length
def test_attributes_chromosome_impl_lambdas():
# Create the Genetic algorithm
ga = GA()
# Set necessary attributes
ga.chromosome_length = 3
ga.generation_goal = 1
# Set gene_impl to None so it won't interfere
ga.gene_impl = None
# Set chromosome_impl
ga.chromosome_impl = lambda: [
random.randrange(1,100),
random.uniform(10,5),
random.choice(["up","down"])
]
# Evolve the genetic algorithm
ga.evolve()
def test_attributes_chromosome_impl_functions():
# Create the Genetic algorithm
ga = GA()
# Set necessary attributes
ga.chromosome_length = 3
ga.generation_goal = 1
# Create chromosome_impl user function
def user_chromosome_function():
chromosome_data = [
random.randrange(1,100),
random.uniform(10,5),
random.choice(["up","down"])
]
return chromosome_data
# Set the chromosome_impl
ga.chromosome_impl = user_chromosome_function
# Evolve the genetic algorithm
ga.evolve()
def easyga(request):
ga = GA()
a = 1 #this number determines how many evolutions there are, will be changed later using form
ga.evolve(a)
context = {'population': ga.population, 'best': ga.population[0]}
return render(request, 'news/easyga.html', context)
