def run_ga(id):
# Also log to file
if args.o:
file_handler = logging.FileHandler(log_dir + "/report%i.log" %
(id + 1))
file_handler.setFormatter(format)
logger.addHandler(file_handler)
# Summary
logger.info("------------------------------GA Settings------------------")
logger.info("Initial population: %i", args.p)
logger.info("Initial population method: %s", args.M)
logger.info("Initial population ratio: %i", args.R)
logger.info("Population restart method: %s", args.S)
logger.info("Population restart ratio: %f", args.r)
logger.info("Elitism: %i", args.e)
if args.k is not None:
logger.info("Tournament selection size: %i", args.k)
elif args.K is not None:
logger.info("Selection pressure (Ranking): %f", args.K)
elif args.P:
logger.info("Pairwise selection")
logger.info("Crossover probability: %f", args.c)
logger.info("Mutation probability: %f", args.m)
logger.info("Mutation operator: %s", args.t)
logger.info("Fusion: %s", args.F)
logger.info("Explore: %s", args.E)
logger.info("Generation limit: %i", args.g)
logger.info("Instance: %s", args.I)
logger.info("Dimension: %i", instance.dimension)
logger.info("Iteration: %i/%i", id + 1, args.n)
# Stats
avg_fitness = defaultdict(list)
best_fitness = defaultdict(list)
best_solution = dict()
counters = defaultdict(list)
timers = defaultdict(list)
# Crossover operator
gpx = GPX(instance)
# Define which tests will be applied
gpx.test_1 = args.t1
gpx.test_2 = args.t2
gpx.test_3 = args.t3
# Fusion, explore and relax switches
gpx.fusion_on = args.F
gpx.explore_on = args.E
gpx.relax = args.L
# Fusion tests
gpx.test_1_fusion = args.t1f
gpx.test_2_fusion = args.t2f
gpx.test_3_fusion = args.t3f
# GA Instance
ga = GA(instance, gpx, args.e)
# Generate inicial population
ga.gen_pop(args.p, args.M, args.R, args.l, args.s)
# Fisrt population evaluation
ga.evaluate()
# Begin GA
while ga.generation < args.g:
# Update plot
if args.n == 1 and args.G:
plt_mgr.add(x=ga.generation,
y=ga.counters['avg_fit'][-1],
name='Average fitness')
plt_mgr.add(x=ga.generation,
y=ga.counters['best_fit'][-1],
name='Best fitness')
plt_mgr.add(x=ga.generation,
y=ga.counters['cross'][-1],
name='Crossover')
plt_mgr.update()
# Logging
avg_fitness[ga.generation].append(ga.counters['avg_fit'][-1])
best_fitness[ga.generation].append(ga.counters['best_fit'][-1])
# Population restart
if args.r:
ga.restart_pop(args.r, args.S)
# Selection
if args.k is not None:
ga.tournament_selection(args.k)
elif args.K is not None:
ga.rank_selection(args.K)
elif args.P:
ga.pairwise_selection()
# Recombination
if args.c:
ga.recombine(args.c, args.P)
# Mutation
if args.m:
ga.mutate(args.m, args.t)
# Evaluation
ga.evaluate()
# Generation info
ga.print_info()
# Final report
ga.report()
# Close ploting
if args.n == 1 and args.G:
plt_mgr.close()
# Best solution
best_solution[id] = ga.best_solution
# Calc improvement
parents_dist = float(gpx.counters['parents_dist'])
children_dist = float(gpx.counters['children_dist'])
improvement = 0
if parents_dist != 0:
improvement = (1 - children_dist / parents_dist) * 100
# Counters
counters[id].extend([
gpx.counters['feasible_1'], gpx.counters['feasible_2'],
gpx.counters['feasible_3'], gpx.counters['feasible'],
gpx.counters['infeasible'], gpx.counters['fusion_1'],
gpx.counters['fusion_2'], gpx.counters['fusion_3'],
gpx.counters['fusion'], gpx.counters['unsolved'],
sum(ga.counters['cross']), gpx.counters['improved'],
gpx.counters['failed_0'], gpx.counters['failed_1'],
gpx.counters['failed_2'], gpx.counters['failed_3'],
gpx.counters['failed'], gpx.counters['inf_tour_0'],
gpx.counters['inf_tour_1'], gpx.counters['inf_tour'], improvement,
sum(ga.counters['mut'])
])
# Timers
timers[id].extend([
sum(ga.timers['total']),
sum(ga.timers['population']),
sum(ga.timers['evaluation']),
sum(ga.timers['tournament']),
sum(ga.timers['recombination']),
sum(gpx.timers['g_star']),
sum(gpx.timers['partitioning']),
sum(gpx.timers['simple_graph']),
sum(gpx.timers['simple_graph_f']),
sum(gpx.timers['classification']),
sum(gpx.timers['fusion']),
sum(gpx.timers['build']),
sum(ga.timers['mutation']),
sum(ga.timers['restart_pop'])
])
# Return data
return avg_fitness, best_fitness, best_solution, counters, timers
