import matplotlib as mpl
import matplotlib.pyplot as plt
from peabox_individual import Individual
from peabox_population import Population
from peabox_testfuncs import CEC05_test_function_producer as tfp
from peabox_helpers import parentselect_exp as pse
from peabox_helpers import simple_searchspace
#-------------------------------------------------------------------------------
#--- 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 = 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()
#-------------------------------------------------------------------------------
#--- intermezzo: the colormap used in peabox_plotting.ancestryplot -------------
#-------------------------------------------------------------------------------
show_these_colormaps([ancestcolors],'colormap_used_for_ancestry_coloring.png')
#-------------------------------------------------------------------------------
#--- part 1: Ingredients -------------------------------------------------------
#-------------------------------------------------------------------------------
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')
def do_step(self):
PSO_Individ.do_step(self)
self.memorize()
def get_trajectory_components(self, dims):
output = []
for d in dims:
output.append([self.traj[i][d] for i in range(len(self.traj))])
return output
dim = 2
f1 = tfp(1, dim) # Weierstrass function in 2 dimensions
f11 = tfp(11, dim) # Weierstrass function in 2 dimensions
searchspace = simple_searchspace(dim, -3., 1.)
p = Pswarm(Memorizing_PSO_Individ, 12, f1, searchspace, [3, 4], 1)
p.random_ini()
#p.alpha=0.95
if True:
dude = p[0]
#dude.random_influence='1D'
dude.add_attractor(ones(dim), 1.)
dude.speed[:] = 1, 0
for i in range(20):
dude.do_step()
x, y = dude.get_trajectory_components([0, 1])
plt.plot(x, y, 'bo-')
def memorize(self):
self.traj.append(self.get_copy_of_DNA())
self.trajqual.append(self.score)
def do_step(self):
PSO_Individ.do_step(self)
self.memorize()
def get_trajectory_components(self,dims):
output=[]
for d in dims:
output.append([self.traj[i][d] for i in range(len(self.traj))])
return output
dim=2
f1=tfp(1,dim) # Weierstrass function in 2 dimensions
f11=tfp(11,dim) # Weierstrass function in 2 dimensions
searchspace=simple_searchspace(dim,-3.,1.)
p=Pswarm(Memorizing_PSO_Individ,12,f1,searchspace,[3,4],1)
p.random_ini()
#p.alpha=0.95
if True:
dude=p[0]
#dude.random_influence='1D'
dude.add_attractor(ones(dim),1.)
dude.speed[:]=1,0
for i in range(20):
dude.do_step()
x,y=dude.get_trajectory_components([0,1])
from comboCbeta import ComboC_beta
from tkframe import TKEA_win
import matplotlib.pyplot as plt
#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)
