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_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 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_stem.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 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 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_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 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_stem.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 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_stem.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
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_stem.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
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