def find_schemas(self, fitness, num_schemas):
"""Find the given number of unique schemas using a genetic algorithm
Arguments:
- fitness - A callable object (ie. function) which will evaluate
the fitness of a motif.
- num_schemas - The number of unique schemas with good fitness
that we want to generate.
"""
start_population = \
Organism.function_population(self.motif_generator.random_motif,
self.initial_population,
fitness)
finisher = SimpleFinisher(num_schemas, self.min_generations)
# set up the evolver and do the evolution
evolver = GenerationEvolver(start_population, self.selector)
evolved_pop = evolver.evolve(finisher.is_finished)
# convert the evolved population into a PatternRepository
schema_info = {}
for org in evolved_pop:
# convert the Genome from a MutableSeq to a Seq so that
# the schemas are just strings (and not array("c")s)
seq_genome = org.genome.toseq()
schema_info[str(seq_genome)] = org.fitness
return PatternRepository(schema_info)
def find_schemas(self, fitness, num_schemas):
"""Find the given number of unique schemas using a genetic algorithm
Arguments:
o fitness - A callable object (ie. function) which will evaluate
the fitness of a motif.
o num_schemas - The number of unique schemas with good fitness
that we want to generate.
"""
start_population = \
Organism.function_population(self.motif_generator.random_motif,
self.initial_population,
fitness)
finisher = SimpleFinisher(num_schemas, self.min_generations)
# set up the evolver and do the evolution
evolver = GenerationEvolver(start_population, self.selector)
evolved_pop = evolver.evolve(finisher.is_finished)
# convert the evolved population into a PatternRepository
schema_info = {}
for org in evolved_pop:
# convert the Genome from a MutableSeq to a Seq so that
# the schemas are just strings (and not array("c")s)
seq_genome = org.genome.toseq()
schema_info[str(seq_genome)] = org.fitness
return PatternRepository(schema_info)
def main(num_queens):
print "Calculating for %s queens..." % num_queens
num_orgs = 1000
print "Generating an initial population of %s organisms..." % num_orgs
queen_alphabet = QueensAlphabet(num_queens)
start_population = Organism.random_population(queen_alphabet, num_queens, num_orgs, queens_fitness)
print "Evolving the population and searching for a solution..."
mutator = QueensMutation(mutation_rate=0.05)
crossover = QueensCrossover(queens_fitness, crossover_prob=0.2, max_crossover_size=4)
repair = QueensRepair()
# rw_selector = RouletteWheelSelection(mutator, crossover, repair)
t_selector = TournamentSelection(mutator, crossover, repair, 5)
start_time = time.ctime(time.time())
evolver = GenerationEvolver(start_population, t_selector)
evolved_pop = evolver.evolve(queens_solved)
end_time = time.ctime(time.time())
unique_solutions = []
for organism in evolved_pop:
if organism.fitness == num_queens:
if organism not in unique_solutions:
unique_solutions.append(organism)
if VERBOSE:
print "Search started at %s and ended at %s" % (start_time, end_time)
for organism in unique_solutions:
print "We did it!", organism
display_board(organism.genome)
def main(num_queens):
print "Calculating for %s queens..." % num_queens
num_orgs = 1000
print "Generating an initial population of %s organisms..." % num_orgs
queen_alphabet = QueensAlphabet(num_queens)
start_population = Organism.random_population(queen_alphabet, num_queens,
num_orgs, queens_fitness)
print "Evolving the population and searching for a solution..."
mutator = QueensMutation(mutation_rate=0.05)
crossover = QueensCrossover(queens_fitness,
crossover_prob=.2,
max_crossover_size=4)
repair = QueensRepair()
# rw_selector = RouletteWheelSelection(mutator, crossover, repair)
t_selector = TournamentSelection(mutator, crossover, repair, 5)
start_time = time.ctime(time.time())
evolver = GenerationEvolver(start_population, t_selector)
evolved_pop = evolver.evolve(queens_solved)
end_time = time.ctime(time.time())
unique_solutions = []
for organism in evolved_pop:
if organism.fitness == num_queens:
if organism not in unique_solutions:
unique_solutions.append(organism)
if VERBOSE:
print "Search started at %s and ended at %s" % (start_time, end_time)
for organism in unique_solutions:
print "We did it!", organism
display_board(organism.genome)
