ps = 40 # population size
G = 80 # number of generations to go through
parents = Population(Individual, ps, f11, searchspace)
offspring = Population(Individual, ps, f11, searchspace)
#-------------------------------------------------------------------------------
#--- 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()
sa_T = parents[-1].score - parents[0].score # starting temperature
saga_ratio = 0.5 # fraction of offspring created by SA and not GA
sa_mstep = 0.02 # mutation step size parameter
sa_mprob = 0.6 # mutation probability
ga_selp = 3. # selection pressure
AE = 0.04 # annealing exponent --> exp(-AE) is multiplier for reducing temperature
elite_size = 0
g_rec = [] # for recording x-data for plot, i.e. generation
bs_rec = [] # for recording y-data for plot, here best score
ws_rec = [] # for recording y-data for plot, here worst score
T_rec = [] # for recording y-data for plot, here temperature
ms_rec = [] # for recording y-data for plot, here mutation step size
# saving initial generation characteristics
print "the population's scores: ", sc # should give the list [ 1.22 1.32 0.53 0.17 1.81]
print 'the population itself gets printed out like this:'
print p
print 'now sorting'
p.sort()
print p
print 'now reversing'
p.reverse()
print p
print '\n'
print 'each dude has a number, its attribute "no", and each one has also a stored former number "oldno"'
print 'dude.no: ', [dude.no for dude in p]
print 'dude.oldno: ', [dude.oldno for dude in p]
print "now let's update the numbering so it corresponds to the current sequence"
p.update_no()
print 'dude.no: ', [dude.no for dude in p]
print 'dude.oldno: ', [dude.oldno for dude in p]
print 'new look of the direct printout: ', p, ' which is the output of "Population.__str__()"'
print 2 * '\n'
print "now let's sort the population according to the first entry of the DNA vector saying p.sort_for('DNA[0]')"
p.sort_for(
'DNA[0]'
) # you can sort_for(somestring) as long as eval('dude.'+somestring) makes some sense
print "you can sort_for(somestring) as long as eval('dude.'+somestring) makes some sense"
print 'dude.no: ', [dude.no for dude in p]
print 'dude.oldno: ', [dude.oldno for dude in p]
print 'dude.DNA[0]: ', [dude.DNA[0] for dude in p]
print "or let's change opinion and sort for the second DNA vector entry, the wall thickness"
print "there is a sorting routine accepting the DNA parameter name: p.sort_for_DNApar('wall_thickness')"
print "the population's scores: ",sc # should give the list [ 1.22 1.32 0.53 0.17 1.81]
print 'the population itself gets printed out like this:'
print p
print 'now sorting'
p.sort()
print p
print 'now reversing'
p.reverse()
print p
print '\n'
print 'each dude has a number, its attribute "no", and each one has also a stored former number "oldno"'
print 'dude.no: ',[dude.no for dude in p]
print 'dude.oldno: ',[dude.oldno for dude in p]
print "now let's update the numbering so it corresponds to the current sequence"
p.update_no()
print 'dude.no: ',[dude.no for dude in p]
print 'dude.oldno: ',[dude.oldno for dude in p]
print 'new look of the direct printout: ',p,' which is the output of "Population.__str__()"'
print 2*'\n'
print "now let's sort the population according to the first entry of the DNA vector saying p.sort_for('DNA[0]')"
p.sort_for('DNA[0]') # you can sort_for(somestring) as long as eval('dude.'+somestring) makes some sense
print "you can sort_for(somestring) as long as eval('dude.'+somestring) makes some sense"
print 'dude.no: ',[dude.no for dude in p]
print 'dude.oldno: ',[dude.oldno for dude in p]
print 'dude.DNA[0]: ',[dude.DNA[0] for dude in p]
print "or let's change opinion and sort for the second DNA vector entry, the wall thickness"
print "there is a sorting routine accepting the DNA parameter name: p.sort_for_DNApar('wall_thickness')"
p.sort_for_DNApar('wall_thickness')
print 'dude.no: ',[dude.no for dude in p]
searchspace=simple_searchspace(10,-3.,1.) ps=40 # population size G=80 # number of generations to go through parents=Population(Individual,ps,f11,searchspace) offspring=Population(Individual,ps,f11,searchspace) #------------------------------------------------------------------------------- #--- 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() sa_T=parents[-1].score-parents[0].score # starting temperature saga_ratio=0.5 # fraction of offspring created by SA and not GA sa_mstep=0.02 # mutation step size parameter sa_mprob=0.6 # mutation probability ga_selp=3. # selection pressure AE=0.04 # annealing exponent --> exp(-AE) is multiplier for reducing temperature elite_size=0 g_rec=[] # for recording x-data for plot, i.e. generation bs_rec=[] # for recording y-data for plot, here best score ws_rec=[] # for recording y-data for plot, here worst score T_rec=[] # for recording y-data for plot, here temperature ms_rec=[] # for recording y-data for plot, here mutation step size # saving initial generation characteristics
