def __init__(self, F0pop, F1pop, userec=False):
self.F0 = F0pop # parent population, nomenclature like in your biology book
self.F1 = F1pop # offspring population, nomenclature like in your biology book
self.sa_T = 1. # temperature
self.saga_ratio = 0.5 # fraction of offspring created by SA and not GA
self.sa_mstep = 0.05 # mutation step size parameter
self.sa_mprob = 0.6 # mutation probability
self.ga_selp = 3. # selection pressure
self.AE = 0.08 # annealing exponent --> exp(-AE) is multiplier for reducing temperature
self.elite_size = 2
self.reduce_mstep = False
if userec:
self.rec = Recorder(F0pop)
self.rec.snames.append(
'sa_improverate'
) # appending a scalar property's name to the survey list
self.rec.snames.append('sa_toleraterate')
self.rec.snames.append('ga_improverate')
self.rec.reinitialize_data_dictionaries()
self.userec = True
else:
self.rec = None
self.userec = False
#-------------------------------------------------------------------------------
dim = 10
f11 = tfp(11, dim) # Weierstrass function in 10 dimensions
searchspace = simple_searchspace(10, -3., 1.)
ps = 40 # population size
G = 40 # number of generations to go through
parents = Population(Individual, ps, f11, searchspace)
parents.objname = 'CEC05 f11'
offspring = Population(Individual, ps, f11, searchspace)
offspring.objname = 'CEC05 f11'
rec = Recorder(
parents
) # creating this Recorder instance to collect survey data on parent population
#-------------------------------------------------------------------------------
#--- part 2: initialisation ----------------------------------------------------
#-------------------------------------------------------------------------------
seed(
1
) # seeding numpy's random number generator so we get reproducibly the same random numbers
parents.new_random_genes()
parents.eval_all()
parents.sort()
parents.update_no()
rec.save_status()
b.plot_FMsynth_solution()
print 'gcb: score: {} DNA: {}'.format(b.score, b.DNA)
print 'gcb: target[:5]: ', b.target[:5]
# search space boundaries:
searchspace = (('amp 1', -6.4, +6.35), ('omega 1', -6.4, +6.35),
('amp 2', -6.4, +6.35), ('omega 2', -6.4, +6.35),
('amp 3', -6.4, +6.35), ('omega 3', -6.4, +6.35))
dim = len(searchspace) # search space dimension
ps_ref = 20
dset = Population(FMsynthC, 40, dummyfunc, searchspace)
rset = Population(FMsynthC, ps_ref, dummyfunc, searchspace)
rec = Recorder(rset)
def gcb2(eaobj):
rec.save_status()
ea = ScatterSearch(dset, rset, nref=ps_ref, b1=10)
ea.refset_update_style = 'Herrera'
ea.generation_callbacks.append(gcb1)
ea.generation_callbacks.append(gcb2)
ea.complete_algo(10)
ancestryplot(rec, ylimits=[0, 70])
plt.close()
def set_bad_score(self):
self.score = 9999.
# search space boundaries:
searchspace = (('amp 1', -6.4, +6.35), ('omega 1', -6.4, +6.35),
('amp 2', -6.4, +6.35), ('omega 2', -6.4, +6.35),
('amp 3', -6.4, +6.35), ('omega 3', -6.4, +6.35))
dim = len(searchspace) # search space dimension
ps = 80
p = Population(FMsynthC, ps, dummyfunc, searchspace)
rec = Recorder(p)
ea = CMAES(p, 40, rec) # instanciate the algorithm from library
ea.maxeval = 1e6
def gcb(eaobj):
if eaobj.F0.gg % 10 == 0: # every 10th generation
b = eaobj.F0[0]
b.plot_FMsynth_solution()
print 'gcb: generation: {} score: {} DNA: {}'.format(
eaobj.F0.gg, b.score, b.DNA)
rec.save_status()
ea.generation_callbacks.append(gcb)
def set_bad_score(self):
self.score = 9999.
# search space boundaries:
searchspace = (('amp 1', -6.4, +6.35), ('omega 1', -6.4, +6.35),
('amp 2', -6.4, +6.35), ('omega 2', -6.4, +6.35),
('amp 3', -6.4, +6.35), ('omega 3', -6.4, +6.35))
dim = len(searchspace) # search space dimension
ps = 80
parents = Population(FMsynthC, ps, dummyfunc, searchspace)
offspring = Population(FMsynthC, ps, dummyfunc, searchspace)
rec = Recorder(parents)
ea = ComboB(parents, offspring) # instanciate the algorithm from library
def gcb(eaobj):
if eaobj.F0.gg % 10 == 0: # every 10th generation
b = eaobj.F0[0]
b.plot_FMsynth_solution()
print 'gcb: generation: {} score: {} DNA: {}'.format(
eaobj.F0.gg, b.score, b.DNA)
rec.save_status()
ea.generation_callbacks.append(gcb)
# set algorithm parameters (meaning of unexplained parameters should be inferred from source code)
#plt.ion()
def dummyfunc(x):
return np.sum(x)
ndim=8
ps=80
problem = 'gf_kragtraeger'
EA_type = 'CMAES'
if problem == 'FMsynth':
space=simple_searchspace(ndim, -6.4, +6.35)
p0=Population(FMsynth,ps,dummyfunc,space)
p1=Population(FMsynth,ps,dummyfunc,space)
rec=Recorder(p0)
elif problem == 'necklace':
space=simple_searchspace(ndim, 0., 360.)
p0=Population(necklace,ps,dummyfunc,space)
p1=Population(necklace,ps,dummyfunc,space)
rec=Recorder(p0)
elif problem == 'hilly':
space=simple_searchspace(ndim, 0., 360.)
p0=Population(hilly,ps,dummyfunc,space)
p1=Population(hilly,ps,dummyfunc,space)
rec=Recorder(p0)
elif problem == 'murmeln':
space=simple_searchspace(ndim, 0., 360.)
