# The Deb function
#f = Deb()
# The Pol function
f = Pol()
# start at the origin
x0 = zeros(f.indim)
x0 = array([min_ for min_, max_ in f.xbound])
# the optimization for a maximum of 25 generations
n = MultiObjectiveGA(f,
x0,
storeAllEvaluations=True,
populationSize=50,
eliteProportion=1.0,
topProportion=1.0,
mutationProb=0.5,
mutationStdDev=0.1,
storeAllPopulations=True,
allowEquality=False)
print 'Start Learning'
n.learn(30)
print 'End Learning'
# plotting the results (blue = all evaluated points, red = resulting pareto front)
print 'Plotting the Results'
print 'All Evaluations'
for x in n._allEvaluations:
pylab.plot([x[0]], [x[1]], 'b.')
for x in n.bestEvaluation:
pylab.plot([x[0]], [x[1]], 'ro')
#!/usr/bin/env python """ An illustration of using the NSGA-II multi-objective optimization algorithm on a simple standard benchmark function. """ __author__ = 'Tom Schaul, [email protected]' from pybrain.optimization import MultiObjectiveGA from pybrain.rl.environments.functions.multiobjective import KurBenchmark import pylab from scipy import zeros # The benchmark function f = KurBenchmark() # start at the origin x0 = zeros(f.indim) # the optimization for a maximum of 25 generations n = MultiObjectiveGA(f, x0, storeAllEvaluations=True) n.learn(25) # plotting the results (blue = all evaluated points, red = resulting pareto front) for x in n._allEvaluations: pylab.plot([x[1]], [x[0]], 'b+') for x in n.bestEvaluation: pylab.plot([x[1]], [x[0]], 'ro') pylab.show()
