def lambdacommamu(pop, Optimizer):
"""Selection function to employ a lambda,mu GA scheme
"""
fits = [ind.fitness for ind in pop]
minindex = [i for i in range(len(pop)) if pop[i].fitness==min(fits)]
try:
Optimizer.mark
except:
Optimizer.mark = len(pop)/2
parents = pop[0:Optimizer.mark]
offspring = pop[Optimizer.mark::]
if minindex < Optimizer.mark:
offspring.append(pop[minindex])
if len(offspring) < Optimizer.nindiv:
diff = Optimizer.nindiv-len(offspring)
if Optimizer.natural_selection_scheme=='elitism':
addins = get_best(parents,diff)
STR = 'Adding in '+repr(diff)+' lowest fitness parents\n'
else:
addins = selection_switch(parents, diff, Optimizer.natural_selection_scheme, Optimizer)
STR = 'Adding in '+repr(diff)+' parents based on natural selection\n'
for one in addins:
offspring.append(one)
elif len(offspring) > Optimizer.nindiv:
diff = len(offspring)-Optimizer.nindiv
if Optimizer.natural_selection_scheme=='elitism':
offspring = get_best(offspring,Optimizer.nindiv)
STR = 'Removing lowest '+repr(diff)+' fitness offspring\n'
else:
offspring = selection_switch(offspring, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
STR = 'Removing '+repr(diff)+' offspring by natural selection\n'
else:
STR = 'Number of offspring = {0}\n'.format(len(offspring))
return offspring, STR
def lambdacommamu(pop, Optimizer):
"""Selection function to employ a lambda,mu GA scheme
"""
fits = [ind.fitness for ind in pop]
minindex = [i for i in range(len(pop)) if pop[i].fitness == min(fits)]
try:
Optimizer.mark
except:
Optimizer.mark = len(pop) / 2
parents = pop[0:Optimizer.mark]
offspring = pop[Optimizer.mark::]
if minindex < Optimizer.mark:
offspring.append(pop[minindex])
if len(offspring) < Optimizer.nindiv:
diff = Optimizer.nindiv - len(offspring)
if Optimizer.natural_selection_scheme == 'elitism':
addins = get_best(parents, diff)
STR = 'Adding in ' + repr(diff) + ' lowest fitness parents\n'
else:
addins = selection_switch(parents, diff,
Optimizer.natural_selection_scheme,
Optimizer)
STR = 'Adding in ' + repr(
diff) + ' parents based on natural selection\n'
for one in addins:
offspring.append(one)
elif len(offspring) > Optimizer.nindiv:
diff = len(offspring) - Optimizer.nindiv
if Optimizer.natural_selection_scheme == 'elitism':
offspring = get_best(offspring, Optimizer.nindiv)
STR = 'Removing lowest ' + repr(diff) + ' fitness offspring\n'
else:
offspring = selection_switch(offspring, Optimizer.nindiv,
Optimizer.natural_selection_scheme,
Optimizer)
STR = 'Removing ' + repr(
diff) + ' offspring by natural selection\n'
else:
STR = 'Number of offspring = {0}\n'.format(len(offspring))
return offspring, STR
def mutation_dups_adapt_stem(pop, Optimizer):
"""Predator function that removes individuals based on fitness and mutates replacements
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.index)
nindices.sort()
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str1
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
indiv = pop[0]
if (indiv.fitness/indiv.energy <2.0):
from MAST.structopt.tools.StemCalc import find_stem_coeff
outs = find_stem_coeff(Optimizer,indiv)
ind = outs[1]
Optimizer.stem_coeff = outs[0]
STR+='Readjusting STEM Coeff = {0}'.format(Optimizer.stem_coeff))
return pop, STR
def adapting(pop, Optimizer):
"""Function to provide an adapting fitness function for GA evaluation
Input:
pop = population consisting of list of Individual Class objects to be evaluated
Optimizer = Optimizer class object with fitness parameters
Output:
pop = new population updated based on fitness evaluation
*** needs work ***
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR += 'Predator: Removed total of ' + repr(
len(fitlist) - len(nfitlist)) + ' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR += 'Predator: Removed ' + repr(pop[i].history_index) + '\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
Optimizer.output.write('Predator: Adding duplicates back')
indiv = random.choice(otherlist)
newpop.append(indiv)
STR += 'Predator: Adding mutated duplicates to new pop history=' + indiv.history_index + '\n'
if Optimizer.natural_selection_scheme == 'fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer, ind,
Optimizer.fpbin,
Optimizer.fpcutoff)
if genrep >= Optimizer.reqrep * Optimizer.adaptbegin:
ofusslim = Optimizer.fusslimit
nfusslim = ofusslim * math.exp(-Optimizer.adaptmultiplier * float(
Optimizer.genrep) / float(Optimizer.reqrep))
Optimizer.fusslimit = nfusslim
else:
ofusslim = Optimizer.fusslimit
pop = selection_switch(newpop, Optimizer.nindiv,
Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop, len(pop))
Optimizer.fusslimit = ofusslim
return pop, STR
def mutation_dups_zp(pop, Optimizer):
"""Predator function that selects individuals that are too similar based fitness and
replaces them with a zero point rotation of the structure
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
mutopts = Optimizer.mutation_options
Optimizer.mutation_options = ['ZP_Rotation']
indiv = moves_switch(indiv, Optimizer)
Optimizer.mutation_options = mutopts
newpop.append(indiv)
nindices.append(indiv.index)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.sort()
if Optimizer.natural_selection_scheme=='FUSSF':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str1
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
return pop, STR
def mutation_dups_energy(pop, Optimizer):
"""Predator function that removes duplicates based on energy and replaces with mutations
"""
fitlist = [one.energy for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.index)
nindices.sort()
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str1
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
return pop, STR
def adapting(pop, Optimizer):
"""Function to provide an adapting fitness function for GA evaluation
Input:
pop = population consisting of list of Individual Class objects to be evaluated
Optimizer = Optimizer class object with fitness parameters
Output:
pop = new population updated based on fitness evaluation
*** needs work ***
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
Optimizer.output.write('Predator: Adding duplicates back')
indiv = random.choice(otherlist)
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if genrep >= Optimizer.reqrep*Optimizer.adaptbegin:
ofusslim = Optimizer.fusslimit
nfusslim = ofusslim*math.exp(-Optimizer.adaptmultiplier*float(Optimizer.genrep)/float(Optimizer.reqrep))
Optimizer.fusslimit = nfusslim
else:
ofusslim = Optimizer.fusslimit
pop = selection_switch(newpop, Optimizer.nindiv,Optimizer.natural_selection_scheme,Optimizer)
pop = get_best(pop,len(pop))
Optimizer.fusslimit=ofusslim
return pop, STR
def generation_set(self,pop):
global logger
self.calc = tools.setup_calculator(self) #Set up calculator for atomic structures
#Set up calculator for fixed region calculations
if self.fixed_region:
self.static_calc = self.calc #May need to copy this
self.calc = tools.setup_fixed_region_calculator(self)
self.output.write('\n-------- Generation '+repr(self.generation)+' --------\n')
self.files[self.nindiv].write('Generation '+str(self.generation)+'\n')
if len(pop) == 0:
logger.info('Initializing structures')
offspring = self.initialize_structures()
self.population = offspring
else:
for i in range(len(pop)):
# Reset History index
pop[i].history_index=repr(pop[i].index)
# Select the next generation individuals
offspring = switches.selection_switch(pop, self.nindiv,
self.selection_scheme, self)
# Clone the selected individuals
offspring=[off1.duplicate() for off1 in offspring]
# Apply crossover to the offspring
self.output.write('\n--Applying Crossover--\n')
cxattempts = 0
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < self.cxpb:
child1,child2 = switches.crossover_switch(child1, child2, self)
cxattempts+=2
self.cxattempts=cxattempts
#DEBUG: Write first child
if 'MA' in self.debug:
inp_out.write_xyz(self.debugfile,offspring[0][0],'First Child '+
repr(offspring[0].history_index))
# Apply mutation to the offspring
self.output.write('\n--Applying Mutation--\n')
mutattempts = []
muts = []
for mutant in offspring:
if random.random() < self.mutpb:
if self.mutant_add:
mutant = mutant.duplicate()
mutant, optsel = switches.moves_switch(mutant,self)
mutattempts.append([mutant.history_index,optsel])
if self.mutant_add:
muts.append(mutant)
if self.mutant_add:
offspring.extend(muts)
self.mutattempts=mutattempts
#DEBUG: Write first offspring
if 'MA' in self.debug:
inp_out.write_xyz(self.debugfile,muts[0][0],'First Mutant '+\
repr(muts[0].history_index))
if 'stem' in self.fitness_scheme:
if self.stem_coeff==None:
logger.info('Setting STEM coeff (alpha)')
ind = offspring.pop()
from MAST.structopt.tools.StemCalc import find_stem_coeff
outs = find_stem_coeff(self,ind)
ind = outs[1]
ind.fitness = 0
ind.energy = 0
self.stem_coeff = outs[0]
logger.info('STEM Coeff = {0}'.format(self.stem_coeff))
self.output.write('stem_coeff Calculated to be: '+repr(self.stem_coeff)+'\n')
offspring.append(ind)
return offspring
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
STR+='Predator: Adding duplicates back\n'
choice = random.choice(otherlist)
if choice.index not in nindices:
newpop.append(choice)
nindices.append(choice.index)
nindices.sort()
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop,str = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
Optimizer.output.write(STR)
return pop
def mutation_dups_quench(pop, Optimizer):
"""Predator function that removes individuals based on fitness and mutates replacements
Also quenches top individuals
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.index)
nindices.sort()
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str1
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
if Optimizer.genrep >10:
from MAST.structopt.moves.quench import quench
import os
olammpsvar = os.environ['LAMMPS_COMMAND']
try:
from mpi4py import MPI
if '-n' in olammpsvar:
lcommand = olammpsvar.split('-n')
lcommand[1]=lcommand[1].split()
nproc = MPI.COMM_WORLD.Get_size()
os.environ['LAMMPS_COMMAND'] = '{0}-n {1} {2}'.format(lcommand[0],nproc,lcommand[1][1])
except:
pass
oqns2 = Optimizer.quench_n_steps_2
Optimizer.quench_n_steps_2 = 100000
opar = Optimizer.parallel
Optimizer.parallel = False
for i in range(3):
pop[i] = quench(pop[i],Optimizer)
Optimizer.quench_n_steps_2 = oqns2
os.environ['LAMMPS_COMMAND'] = olammpsvar
Optimizer.parallel = opar
return pop, STR
def mutation_dups_quench(pop, Optimizer):
"""Predator function that removes individuals based on fitness and mutates replacements
Also quenches top individuals
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR += 'Predator: Removed total of ' + repr(
len(fitlist) - len(nfitlist)) + ' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR += 'Predator: Removed ' + repr(pop[i].history_index) + '\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv, Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR += 'Predator: Adding mutated duplicates to new pop history=' + indiv.history_index + '\n'
nindices.append(indiv.index)
nindices.sort()
if Optimizer.natural_selection_scheme == 'fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer, ind,
Optimizer.fpbin,
Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [
index for index, n in enumerate(nindices)
if n > Optimizer.nindiv - 1
][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR += str1
else:
pop = selection_switch(newpop, Optimizer.nindiv,
Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop, len(pop))
if Optimizer.genrep > 10:
from MAST.structopt.moves.quench import quench
import os
olammpsvar = os.environ['LAMMPS_COMMAND']
try:
from mpi4py import MPI
if '-n' in olammpsvar:
lcommand = olammpsvar.split('-n')
lcommand[1] = lcommand[1].split()
nproc = MPI.COMM_WORLD.Get_size()
os.environ['LAMMPS_COMMAND'] = '{0}-n {1} {2}'.format(
lcommand[0], nproc, lcommand[1][1])
except:
pass
oqns2 = Optimizer.quench_n_steps_2
Optimizer.quench_n_steps_2 = 100000
opar = Optimizer.parallel
Optimizer.parallel = False
for i in range(3):
pop[i] = quench(pop[i], Optimizer)
Optimizer.quench_n_steps_2 = oqns2
os.environ['LAMMPS_COMMAND'] = olammpsvar
Optimizer.parallel = opar
return pop, STR