def optimisation(N=80, G=100, p_c=0.7, p_m=0.001, log=False, gui=False):
fit_eval = FitnessEvaluator()
if log:
print('Performance optimisation')
print('------------------------')
print('Genetic algorithm:')
print('Population: N = ', N)
print('Generations: G = ', G)
print('Crossover probability: p_c = ', p_c)
print('Mutation probability: p_m = ', p_m)
if gui:
p_graph.init('Performance profile',
f'p_c = {p_c} p_m = {p_m} N = {N} G = {G}')
# Generates random population
perf_run = GeneticRun(gc.length(gc.default_gene_format), N)
if log:
print('\nGenerated initial population')
print('Generation: 0\n')
# Evolution loop
while perf_run.generation < G:
perf_run.evolve(fit_eval.performance, p_c, p_m)
# if log:
# print(f'Generation: {perf_run.generation}')
# print(f'Average fitness: {perf_run.record[-1]["avg_fitness"]}')
# print(f'Max fitness: {perf_run.record[-1]["max_fitness"]}\n')
if gui:
p_graph.update(perf_run.record)
# Present best specimen so far
best_specimen_ch = max(perf_run.record,
key=lambda x: x['max_fitness'])['fittest_specimen']
best_specimen = gc.decode(best_specimen_ch)
distance, fuel, speed = fit_eval.simulate(best_specimen_ch, tries=10)
if log:
print('-------------')
print('Best specimen')
print('-------------')
print('Best chromosones:\n', best_specimen_ch)
print('Best parameters:')
pp.pprint(best_specimen)
print('Specimen performance:')
print(' Distance (m):', distance)
print(' Fuel use (l):', fuel)
print(' Speed (km/h):', speed)
if gui:
p_graph.join()
# Return best specimen
return best_specimen
def __init__(self, size, mu, log):
self.population = []
self.size = size
self.lamb = size
self.mu = mu
self.fiteval = FitnessEvaluator(8)
self.number = 1
self.log = log
self.results_folder = "./results"
self.tournament_rounds = 3
self.mutation_rate = 0.20
class Generation(object):
"""
A generation houses a collection of solutions.
"""
def __init__(self, size, mu, log):
self.population = []
self.size = size
self.lamb = size
self.mu = mu
self.fiteval = FitnessEvaluator(8)
self.number = 1
self.log = log
self.results_folder = "./results"
self.tournament_rounds = 3
self.mutation_rate = 0.20
def random(self, sourcefile, perturb):
starting_data = json.load(sourcefile)
# create random solutions with these parameters
for i in range(0,self.size):
self.population.append(Solution(starting_data, self.number, perturb))
def load_population(self, population):
self.population = population
def reproduce(self):
"""
Create mu children, add them into our generation. Those who reproduce are
determined by a tournament. The best always get a chance.
"""
self.log.debug("REPRODUCTION")
ranked = self.tournament(self.tournament_rounds)
#self.log.debug(pprint.pformat(ranked))
children = []
for l in ranked:
if len(l) == 0:
ranked.remove(l)
for i in range(self.mu):
parents = []
for i in range(2):
# Randomly select from hierarchy, upper has the most chance for individuals
# as there are fewer members... all hierarchies have equal chance.
level = random.sample(ranked,1)[0]
# individuals are then chosen randomly, weighted by fitness (W:L)
#parents.append(choice(level, [x.fitness for x in level]))
# parents chosen randomly
parents.append(random.sample(level,1)[0])
children.append(copulate(self.number, *parents))
for child in children:
if random.random() <= self.mutation_rate:
child.mutate()
#plus strategy
self.population += children
def tournament(self, rounds):
"""
Rather than running a tournament size times, I've elected to do a single tournament
(3 rounds per by default) that then returns a list of ranked individuals.
This list returned is 2d. Starting with the best, each list contains 2*last
entries for where contestants were eliminated.
"""
participants = copy(self.population)
losers = []
winners = []
while len(participants) > 1:
self.log.debug("\nPARTS " + str(len(participants)) + ": ")
random.shuffle(participants)
#self.log.debug(pprint.pformat(participants))
self.output_statistics()
args = []
winners = []
# pair individuals
for i in range(0, len(participants), 2):
if i + 1 >= len(participants):
break
args.append((participants[i], participants[i+1], rounds,))
# get results, add to losers
results = self.fiteval.run(args, get_winner)
for x,a in zip(results, args):
winners.append(self.handle_winners(x,a))
# if we have an odd individual, they compete against a random winner
if len(participants) % 2 != 0:
opponent = random.sample(winners, 1)[0]
args = [(opponent,participants[-1],rounds,)]
result = self.fiteval.run(args, get_winner)
winner = self.handle_winners(result[0], args[0])
if winner is not opponent: # they get to be included... no risk to winner
winners.append(participants[-1])
losers.append([x for x in participants if x not in winners])
#self.log.debug("losers:")
#self.log.debug(pprint.pformat(losers))
participants = winners
losers.append(winners)
losers.reverse()
return losers
def handle_winners(self, results, participants):
"""
Have to handle these on thread.
We're passed a packed results list... 1 for participants[0] winning, 2 for participants[1]
and we tally up how that affects each.
"""
for i in results:
if i == 1:
participants[0].handle_win(participants[1])
participants[0].wins += 1
participants[1].losses += 1
else:
participants[1].handle_win(participants[0])
participants[1].wins += 1
participants[0].losses += 1
if results.count(1) > results.count(2):
return participants[0]
else:
return participants[1]
def natural_selection(self):
"""
Determine who to kill off. Currently done by tournament selection.
"""
newpop = []
self.log.debug("NATURAL SELECTION")
# another tournament
results = self.tournament(self.tournament_rounds)
self.log.debug("RESULTS: ")
#self.log.debug(pprint.pformat(results))
# first 2 winners will not be eliminated
for x in results[:2]:
newpop.extend(x)
for s in x:
#self.log.debug(s.get_config_file())
if s in self.population: #this shouldn't be necessary
self.population.remove(s)
#self.log.debug("POP: ")
#self.log.debug(pprint.pformat(self.population))
self.population = sorted(self.population, key=lambda p: p.fitness, reverse=True)
# top 3 ELO, if not winners, will not be eliminated
for i in range(3):
newpop.append(self.population.pop())
results = results[2:]
# sort our levels
for l in results:
l = sorted(l, key=lambda p: p.fitness, reverse=True)
# now randomly pop elements off
while len(newpop) < self.lamb:
l = random.sample(results,1)[0]
newpop.append(l.pop())
if len(l) == 0:
results.remove(l)
self.population = newpop
def every_ten_tournament(self):
"""
Every 10 generations we have a tournament. It's best out of 7
and the top 7 are output to results/every10/tournament_gen#.txt
"""
self.log.debug("\nDetermining Top 7\n")
ranked = self.tournament(7)
winners = []
for x in ranked[:3]:
winners.extend(x)
filename = os.path.join("generation_{0}.txt".format(self.number))
self.output_solutions_to_file(winners, filename)
def output_solutions_to_file(self, solutions, filename):
"""
Takes a list of solutions and outputs them to a filename in results/.
"""
if not os.path.exists(os.path.abspath(self.results_folder)):
os.mkdir(os.path.abspath(self.results_folder))
filename = os.path.join(os.path.abspath(self.results_folder),filename)
with open(filename,'w') as f:
f.write("Gen\tFit\tWins\tLosses\tTime\n")
for s in solutions:
f.write("{0}\t{1}\t{2}\t{3}\t{4}\n {5}\n".format(s.generation, round(s.fitness), s.wins, s.losses, \
round(s.average_time,2), s.get_config_file()))
def output_statistics(self):
avgtime = 0
avgfitness = 0
avggen = 0
best = None
for s in self.population:
if len(s.times) > 0:
avgtime += s.average_time
avgfitness += s.fitness
if best is None or s.fitness > best.fitness:
best = s
avggen += s.generation
avgfitness /= len(self.population)
avgtime /= len(self.population)
avggen /= len(self.population)
print("\nType\tTime\tFit\tGen\tPop/W:L")
print("Avg\t{0}\t{1}\t{2}\t{3}".format(round(avgtime,2),round(avgfitness,2),round(avggen,2), len(self.population)))
print("Best\t{0}\t{1}\t{2}\t{3}/{4}".format(round(best.average_time,2),round(best.fitness,2),round(best.generation,2),best.wins,best.losses))
# Go up 3 lines
sys.stdout.write("\033[K\033[K\033[K")