def main():
import copy
from boost.mpi import world
import numpy
from pylada.ga import darwin as dd, bitstring, standard, ce
class Darwin:
pass
def stop_at_zero(self):
for indiv in self.population:
if indiv.fitness == 0e0:
return False
return True
bitstring.Individual.size = 80
darwin = Darwin()
darwin.comm = world
darwin.comm.do_print = darwin.comm.rank == 0
evaluation = Eval()
evaluation.target = numpy.array([1 for u in xrange(bitstring.Individual.size)])
if darwin.comm.do_print:
print "Target: ", evaluation.target
darwin.evaluation = standard.mpi_population_evaluation(darwin, evaluation)
def print_nb_eval(darwin):
if not darwin.comm.do_print:
return True
print "Number of functional evaluations: ", evaluation.nbevals
return True
darwin.checkpoints = [
standard.print_offspring,
standard.average_fitness,
standard.best,
print_nb_eval,
stop_at_zero,
]
mating = standard.Mating(sequential=False)
mating.add(bitstring.Crossover(rate=0.25), rate=0.8)
mating.add(bitstring.Mutation(rate=3e0 / float(bitstring.Individual.size)), rate=0.2)
darwin.mating = standard.bound_method(darwin, standard.Mating(sequential=True))
darwin.mating.add(mating, rate=0.8)
darwin.taboo = standard.bound_method(darwin, standard.Taboo(diversity=True))
darwin.rate = 0.1
darwin.popsize = 100
darwin.max_gen = 300
dd.run(darwin)
def main():
from pylada.ga import darwin as dd, bitstring, standard, ce
from pylada import crystal
import boost.mpi
import numpy
import copy
# evaluation class which keeps tracks of the number of calls.
class Eval(ce.EvalFitPairs):
def __init__(self, *args, **kwargs):
ce.EvalFitPairs.__init__(self, *args, **kwargs)
self.nbevals = 0
def __call__(self, indiv):
self.nbevals += 1
return ce.EvalFitPairs.__call__(self, indiv)
class PrintNbEvals:
def __init__(self, evaluation):
self.evaluation = evaluation
def __call__(self, darwin):
print "Number of functional evaluations: ", self.evaluation.nbevals
return True
# creates lattice
lattice = crystal.Lattice("data/lattice.xml")
# the evaluations (CE fitting) class.
evaluation = Eval( lattice=lattice,
path = "data",
lmo_ratio=0.3333333,
pairs = (5, 20, 5),
B3=9, B4=5, B5=2, B6=2)
# the darwin class holding all ga parameters.
class Darwin: pass
darwin = Darwin()
# size of the individuals.
ce.Individual.size = len(evaluation)
# Maximum number of bits when initializing individuals.
ce.Individual.max_mbs_oninit = min(ce.Individual.size, 15)
darwin.Individual = ce.Individual
darwin.evaluation = standard.population_evaluation( darwin, evaluation )
darwin.checkpoints = [ standard.print_offspring,
standard.average_fitness,
standard.best,
PrintNbEvals(evaluation) ]
mating = standard.Mating(sequential=False)
mating.add( ce.SetOver(rate=0.5, inclusive=True), rate=1 )
mating.add( bitstring.Crossover(rate=0.5), rate=1 )
mating.add( bitstring.Mutation(rate=2e0/float(ce.Individual.size)), rate=0.2 )
darwin.mating = standard.bound_method(darwin, standard.Mating(sequential=True))
darwin.mating.add( mating, rate=0.9 )
itermax = 20 # int( float(len(evaluation)) * 1.5 )
darwin.mating.add( bitstring.LocalSearch(evaluation, darwin, itermax=itermax), rate=0.8 )
darwin.taboo = standard.bound_method(darwin, standard.Taboo(diversity=True))
darwin.taboo.add( ce.Taboo(maxmbs=15) ) # constrains to less than maxmbs+1 manybodies
darwin.rate = 0.2
darwin.popsize = 20
darwin.max_gen = 3000
dd.run(darwin)
