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
mu=4 # size of parent population of (mu,lambda)-ES la=12 # size of offspring population of (mu,lambda)-ES dim=len(searchspace) parents=Population(Individual,mu,parabolic,searchspace) offspring=Population(Individual,la,parabolic,searchspace) #------------------------------------------------------------------------------- #--- part 2: random starting point for search ---------------------------------- #--- and more initialisation --------------------------------------------------- #------------------------------------------------------------------------------- npr.seed(1) # seeding numpy's random number generator so we get reproducibly the same random numbers startpoint=2*npr.rand(dim)-1 # point in search space where initial population is placed parents.set_every_DNA(startpoint) parents.eval_all() print 'fitness of initial population: ' print [dude.score for dude in parents] print 'or also via get_scores():' print parents.get_scores() mstep=0.002 # mutation step size parameter print 'initial mstep: ',mstep, 2*'\n' g_rec=[] # for recording x-data for plot, i.e. generation s_rec=[] # for recording y-data for plot, i.e. score or fitness ms_rec=[] # for recording mutation step size history #------------------------------------------------------------------------------- #--- part 3: the loop for (mu,la)-ES ------------------------------------------- #-------------------------------------------------------------------------------
from peabox_individual import Individual
from peabox_population import Population
def parabolic(x):
return np.sum(x*x)
searchspace=(('length',12.,14.),
('wall_thickness',0.1,1.4),
('radius',20.,40.))
N=5
p=Population(Individual,N,parabolic,searchspace)
npr.seed(3)
p.new_random_genes()
p.eval_all()
for dude in p:
dude.score=np.round(dude.score,2)
sc=p.get_scores()
print "the population's scores: ",sc # should give the list [ 1.22 1.32 0.53 0.17 1.81]
dude0,dude1,dude2,dude3,dude4=p
print 'dude0<dude1 yields ',dude0<dude1,' and dude0.isbetter(dude1) yields ',dude0.isbetter(dude1)
print 'dude0<dude2 yields ',dude0<dude2,' and dude0.isbetter(dude2) yields ',dude0.isbetter(dude2)
print 'p.whatisfit and dude0.whatisfit are: ',p.whatisfit,dude0.whatisfit
p.determine_whatisfit('max')
print "now how does it look like after the spell 'p.determine_whatisfit('max')'?"
print 'p.whatisfit and dude0.whatisfit are: ',p.whatisfit,dude0.whatisfit
print 'and the comparisons from above?'
print 'dude0<dude1 yields ',dude0<dude1,' and dude0.isbetter(dude1) yields ',dude0.isbetter(dude1)
print 'dude0<dude2 yields ',dude0<dude2,' and dude0.isbetter(dude2) yields ',dude0.isbetter(dude2)
from peabox_population import Population
def parabolic(x):
return np.sum(x * x)
searchspace = (('length', 12., 14.), ('wall_thickness', 0.1, 1.4), ('radius',
20., 40.))
N = 5
p = Population(Individual, N, parabolic, searchspace)
npr.seed(3)
p.new_random_genes()
p.eval_all()
for dude in p:
dude.score = np.round(dude.score, 2)
sc = p.get_scores()
print "the population's scores: ", sc # should give the list [ 1.22 1.32 0.53 0.17 1.81]
dude0, dude1, dude2, dude3, dude4 = p
print 'dude0<dude1 yields ', dude0 < dude1, ' and dude0.isbetter(dude1) yields ', dude0.isbetter(
dude1)
print 'dude0<dude2 yields ', dude0 < dude2, ' and dude0.isbetter(dude2) yields ', dude0.isbetter(
dude2)
print 'p.whatisfit and dude0.whatisfit are: ', p.whatisfit, dude0.whatisfit
p.determine_whatisfit('max')
print "now how does it look like after the spell 'p.determine_whatisfit('max')'?"
print 'p.whatisfit and dude0.whatisfit are: ', p.whatisfit, dude0.whatisfit
print 'and the comparisons from above?'
self.fmwave([DNA[0], DNA[2], DNA[4]], [DNA[1], DNA[3], DNA[5]])
return np.sum((self.trial - self.target)**2)
# 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))
ps = 4 # population size
dim = len(searchspace) # search space dimension
# now let's create a population to test version A of the objective function implementation
pA = Population(Individual, ps, FMsynthA, searchspace)
pA.new_random_genes()
pA.eval_all()
print 'DNAs of pA:'
print pA.get_DNAs()
pA.print_stuff(slim=True)
# and here the objective function version B and another population to test it
problem_instance = FMsynthB()
objfuncB = problem_instance.call
pB = Population(Individual, ps, objfuncB, searchspace)
pB.copy_otherpop(pA)
pB.eval_all()
print 'DNAs of pB:'
print pB.get_DNAs()
pB.print_stuff(slim=True)
# -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
# 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))
ps=4 # population size
dim=len(searchspace) # search space dimension
pC=Population(FMsynthC,ps,dummyfunc,searchspace)
pC.set_ncase(1)
pC.new_random_genes()
pC.eval_all()
print 'DNAs of pC:'
print pC.get_DNAs()
pC.print_stuff(slim=True)
for dude in pC:
dude.plot_FMsynth_solution()
# and the modified version
pD=Population(FMsynthC,ps,dummyfunc,searchspace)
pD.set_ncase(2)
for dude in pD:
dude.w[1]=4.7
dude.initialize_target()
pD.copy_otherpop(pC)
pD.eval_all()
# 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))
ps=4 # population size
dim=len(searchspace) # search space dimension
# now let's create a population to test version A of the objective function implementation
pA=Population(Individual,ps,FMsynthA,searchspace)
pA.new_random_genes()
pA.eval_all()
print 'DNAs of pA:'
print pA.get_DNAs()
pA.print_stuff(slim=True)
# and here the objective function version B and another population to test it
problem_instance=FMsynthB()
objfuncB=problem_instance.call
pB=Population(Individual,ps,objfuncB,searchspace)
pB.copy_otherpop(pA)
pB.eval_all()
print 'DNAs of pB:'
print pB.get_DNAs()
pB.print_stuff(slim=True)
