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
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 = 1.0 # 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 = 3.0 # 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()
#-------------------------------------------------------------------------------
dim=10
f11=tfp(11,dim) # Weierstrass function in 10 dimensions
searchspace=simple_searchspace(10,-3.,1.)
ps=80 # population size
G=120 # number of generations to go through
parents=SAGA_Population(Individual,ps,f11,searchspace); parents.objname='CEC05 f11'
offspring=SAGA_Population(Individual,ps,f11,searchspace); offspring.objname='CEC05 f11'
parents.ncase=1 # let this be case 1, plots and pickles will contain the number
offspring.ncase=1
rec=Recorder(parents) # creating this Recorder instance to collect survey data on parent population
rec.snames.append('sa_improverate') # appending a scalar property's name to the survey list
rec.snames.append('sa_toleraterate')
rec.snames.append('ga_improverate')
rec.snames.append('sa_bestcontributions')
rec.snames.append('ga_bestcontributions')
rec.reinitialize_data_dictionaries()
rec.set_goalvalue(97.) # note down whether/when this goal was reached
#-------------------------------------------------------------------------------
#--- 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()
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])
class SAGA:
"""
a concoction of simulated annealing plugged into a GA framework
"""
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
def initialize_temperature(self):
# assumption: parent population already evaluated and sorted
self.sa_T = np.fabs(self.F0[-1].score - self.F0[0].score)
def create_offspring(self):
self.F1.advance_generation()
self.F0.reset_event_counters()
for i, dude in enumerate(self.F1):
oldguy = self.F0[i]
if i < self.elite_size:
dude.copy_DNA_of(oldguy, copyscore=True)
dude.ancestcode = 0.18 # blue-purple for conserved dudes
elif rand() < self.saga_ratio:
self.F0.sa_events += 1
dude.copy_DNA_of(oldguy)
dude.mutate(self.sa_mprob, sd=self.sa_mstep)
dude.evaluate()
if dude.isbetter(oldguy):
self.F0.sa_improved += 1
dude.ancestcode = 0.49 # turquoise for improvement through mutation
elif rand() < exp(
-np.fabs(dude.score - oldguy.score) / self.sa_T
): # use of fabs makes it neutral to whatever self.F0.whatisfit
self.F0.sa_tolerated += 1
dude.ancestcode = 0.25 # yellow/beige for tolerated dude
else:
dude.ancestcode = 0.18 # blue-purple for conserved dudes
dude.copy_DNA_of(oldguy,
copyscore=True) # preferring parent DNA
else:
self.F0.ga_events += 1
pa, pb = parentselect_exp(self.F0.psize, self.ga_selp, size=2)
dude.CO_from(self.F0[pa], self.F0[pb])
dude.evaluate()
dude.ancestcode = 0.39 # white for CO dude
# possible changes: accept CO dude only after SA criterion else take better parent (and mutate?)
if dude.isbetter(self.F0[pa]) and dude.isbetter(self.F0[pb]):
self.F0.ga_improved += 1
self.F0.calculate_event_rates()
self.F0.neval += self.F0.psize - self.elite_size # manually updating counter of calls to the objective function
# sorry about the line above, I see this is really just a messy workaround
# there are actually two reasons:
# a) we evaluated dudes of F1 and we act up as if it happened with F0, but it is even messier
# b) so far Population.neval is automatically updated only when using a Population method like
# Population.eval_all() or eval_bunch(), but here we directly had to use the Individual's method evaluate()
def advance_generation(self):
for pdude, odude in zip(self.F0, self.F1):
pdude.copy_DNA_of(odude,
copyscore=True,
copyancestcode=True,
copyparents=True)
self.F0.advance_generation()
def do_step(self):
self.create_offspring()
self.advance_generation()
self.F0.mark_oldno()
self.F0.sort()
self.F0.update_no()
if self.userec: self.rec.save_status()
def run(self, generations):
for i in range(generations):
self.do_step()
self.sa_T *= exp(-self.AE)
if self.reduce_mstep:
self.sa_mstep *= exp(-self.AE)
def simple_run(self, generations, Tstart=None):
self.F0.new_random_genes()
self.F0.zeroth_generation()
if Tstart is not None: self.sa_T = Tstart
else: self.initialize_temperature()
if self.userec: self.rec.save_status()
self.run(generations)
f11 = tfp(11, dim) # Weierstrass function in 10 dimensions
searchspace = simple_searchspace(10, -3., 1.)
ps = 80 # population size
G = 120 # number of generations to go through
parents = SAGA_Population(Individual, ps, f11, searchspace)
parents.objname = 'CEC05 f11'
offspring = SAGA_Population(Individual, ps, f11, searchspace)
offspring.objname = 'CEC05 f11'
parents.ncase = 1 # let this be case 1, plots and pickles will contain the number
offspring.ncase = 1
rec = Recorder(
parents
) # creating this Recorder instance to collect survey data on parent population
rec.snames.append(
'sa_improverate') # appending a scalar property's name to the survey list
rec.snames.append('sa_toleraterate')
rec.snames.append('ga_improverate')
rec.snames.append('sa_bestcontributions')
rec.snames.append('ga_bestcontributions')
rec.reinitialize_data_dictionaries()
rec.set_goalvalue(97.) # note down whether/when this goal was reached
#-------------------------------------------------------------------------------
#--- part 2: initialisation ----------------------------------------------------
#-------------------------------------------------------------------------------
seed(
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)
class SAGA:
"""
a concoction of simulated annealing plugged into a GA framework
"""
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 = 1.0 # 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 = 3.0 # 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
def initialize_temperature(self):
# assumption: parent population already evaluated and sorted
self.sa_T = np.fabs(self.F0[-1].score - self.F0[0].score)
def create_offspring(self):
self.F1.advance_generation()
self.F0.reset_event_counters()
for i, dude in enumerate(self.F1):
oldguy = self.F0[i]
if i < self.elite_size:
dude.copy_DNA_of(oldguy, copyscore=True)
dude.ancestcode = 0.18 # blue-purple for conserved dudes
elif rand() < self.saga_ratio:
self.F0.sa_events += 1
dude.copy_DNA_of(oldguy)
dude.mutate(self.sa_mprob, sd=self.sa_mstep)
dude.evaluate()
if dude.isbetter(oldguy):
self.F0.sa_improved += 1
dude.ancestcode = 0.49 # turquoise for improvement through mutation
elif rand() < exp(
-np.fabs(dude.score - oldguy.score) / self.sa_T
): # use of fabs makes it neutral to whatever self.F0.whatisfit
self.F0.sa_tolerated += 1
dude.ancestcode = 0.25 # yellow/beige for tolerated dude
else:
dude.ancestcode = 0.18 # blue-purple for conserved dudes
dude.copy_DNA_of(oldguy, copyscore=True) # preferring parent DNA
else:
self.F0.ga_events += 1
pa, pb = parentselect_exp(self.F0.psize, self.ga_selp, size=2)
dude.CO_from(self.F0[pa], self.F0[pb])
dude.evaluate()
dude.ancestcode = 0.39 # white for CO dude
# possible changes: accept CO dude only after SA criterion else take better parent (and mutate?)
if dude.isbetter(self.F0[pa]) and dude.isbetter(self.F0[pb]):
self.F0.ga_improved += 1
self.F0.calculate_event_rates()
self.F0.neval += self.F0.psize - self.elite_size # manually updating counter of calls to the objective function
# sorry about the line above, I see this is really just a messy workaround
# there are actually two reasons:
# a) we evaluated dudes of F1 and we act up as if it happened with F0, but it is even messier
# b) so far Population.neval is automatically updated only when using a Population method like
# Population.eval_all() or eval_bunch(), but here we directly had to use the Individual's method evaluate()
def advance_generation(self):
for pdude, odude in zip(self.F0, self.F1):
pdude.copy_DNA_of(odude, copyscore=True, copyancestcode=True, copyparents=True)
self.F0.advance_generation()
def do_step(self):
self.create_offspring()
self.advance_generation()
self.F0.mark_oldno()
self.F0.sort()
self.F0.update_no()
if self.userec:
self.rec.save_status()
def run(self, generations):
for i in range(generations):
self.do_step()
self.sa_T *= exp(-self.AE)
if self.reduce_mstep:
self.sa_mstep *= exp(-self.AE)
def simple_run(self, generations, Tstart=None):
self.F0.new_random_genes()
self.F0.zeroth_generation()
if Tstart is not None:
self.sa_T = Tstart
else:
self.initialize_temperature()
if self.userec:
self.rec.save_status()
self.run(generations)
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.)
#------------------------------------------------------------------------------- #--- part 1: Ingredients ------------------------------------------------------- #------------------------------------------------------------------------------- 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() sa_T=parents[-1].score-parents[0].score # starting temperature saga_ratio=0.5 # fraction of offspring created by SA and not GA
