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)
ea.set_bunchsizes([8, 12, 10, 10, 20, 20])
rec=Recorder(p0)
elif problem == 'rastrigin':
space=simple_searchspace(ndim, -6., 6.)
p0=Population(rastrigin,ps,dummyfunc,space)
p1=Population(rastrigin,ps,dummyfunc,space)
rec=Recorder(p0)
elif problem == 'sphere':
space=simple_searchspace(ndim, -2., 2.)
p0=Population(sphere,ps,dummyfunc,space)
p1=Population(sphere,ps,dummyfunc,space)
rec=Recorder(p0)
if EA_type == 'THEA':
eac=ComboB(p0,p1)
bs=eac.bunching()
eac.set_bunchsizes(bs)
eac.make_bunchlists()
varlist=[]
varlist.append({'name':'selpC', 'type':float, 'inival':1.0})
varlist.append({'name':'selpD', 'type':float, 'inival':2.0})
varlist.append({'name':'selpE', 'type':float, 'inival':2.0})
varlist.append({'name':'selpF', 'type':float, 'inival':3.0})
varlist.append({'name':'mstep', 'type':float, 'inival':0.18})
varlist.append({'name':'Pm', 'type':float, 'inival':0.6})
varlist.append({'name':'mr', 'type':float, 'inival':0.5})
varlist.append({'name':'cigar2uniform', 'type':float, 'inival':0.3})
varlist.append({'name':'cigar_aspect', 'type':float, 'inival':0.3})
varlist.append({'name':'WHX2BLX', 'type':float, 'inival':0.0})
