def advance_generation(self):
self.mark_oldno()
self.sort()
self.update_no()
for dude in self:
k,l=self.imap[dude.oldno]
self.map[k,l]=dude.no
self.update_imap()
Population.advance_generation(self)
def advance_generation(self):
self.mark_oldno()
self.sort()
self.update_no()
for dude in self:
k, l = self.imap[dude.oldno]
self.map[k, l] = dude.no
self.update_imap()
Population.advance_generation(self)
# saving initial generation characteristics
g_rec.append(parents.gg)
bs_rec.append(parents[0].score)
ws_rec.append(parents[-1].score)
T_rec.append(sa_T)
ms_rec.append(sa_mstep)
#-------------------------------------------------------------------------------
#--- part 3: the generational loop ---------------------------------------------
#-------------------------------------------------------------------------------
for g in range(G):
# step A: creating and evaluating offspring
offspring.advance_generation()
for i, dude in enumerate(offspring):
oldguy = parents[i]
if i < elite_size:
dude.copy_DNA_of(oldguy, copyscore=True)
elif rand() < saga_ratio:
dude.copy_DNA_of(oldguy)
dude.mutate(sa_mprob, sd=sa_mstep)
dude.evaluate()
if dude < oldguy:
pass # accepting improvement
elif rand() < exp(-(dude.score - oldguy.score) / sa_T):
pass # accepting improvement
else:
dude.copy_DNA_of(oldguy,
copyscore=True) # preferring parent DNA
g_rec.append(g)
s_rec.append(dude.score)
ms_rec.append(mstep)
successrate=np.sum(successes)/float(np.size(successes))
if successrate > 1./5: # 1/5th success rule is popular because reasonable for evolution strategies
mstep*=1.05
else:
mstep/=1.05
offspring.sort()
for i in range(mu):
parents[i].copy_DNA_of(offspring[i],copyscore=True)
if mod(g+1,20)==0:
parents[0].plot_yourself(plotpath)
parents.advance_generation()
print 'fitness of final population: '
print parents.get_scores()
print 'final mstep: ',mstep, 2*'\n'
#-------------------------------------------------------------------------------
#--- part 4: making a plot showing ---------------------------------------------
#--- a) fitness over time and --------------------------------------------------
#--- b) step size over time ----------------------------------------------------
#-------------------------------------------------------------------------------
N=len(s_rec)
g_rec=np.array(g_rec)-0.4+0.8*npr.rand(N) # a little random horizontal whiggeling for better visibility
# saving initial generation characteristics
g_rec.append(parents.gg)
bs_rec.append(parents[0].score)
ws_rec.append(parents[-1].score)
T_rec.append(sa_T)
ms_rec.append(sa_mstep)
#-------------------------------------------------------------------------------
#--- part 3: the generational loop ---------------------------------------------
#-------------------------------------------------------------------------------
for g in range(G):
# step A: creating and evaluating offspring
offspring.advance_generation()
for i,dude in enumerate(offspring):
oldguy=parents[i]
if i < elite_size:
dude.copy_DNA_of(oldguy,copyscore=True)
elif rand() < saga_ratio:
dude.copy_DNA_of(oldguy)
dude.mutate(sa_mprob,sd=sa_mstep)
dude.evaluate()
if dude<oldguy:
pass # accepting improvement
elif rand() < exp(-(dude.score-oldguy.score)/sa_T):
pass # accepting improvement
else:
dude.copy_DNA_of(oldguy,copyscore=True) # preferring parent DNA
else:
g_rec.append(g)
s_rec.append(dude.score)
ms_rec.append(mstep)
successrate = np.sum(successes) / float(np.size(successes))
if successrate > 1. / 5: # 1/5th success rule is popular because reasonable for evolution strategies
mstep *= 1.05
else:
mstep /= 1.05
offspring.sort()
for i in range(mu):
parents[i].copy_DNA_of(offspring[i], copyscore=True)
if mod(g + 1, 20) == 0:
parents[0].plot_yourself(plotpath)
parents.advance_generation()
print 'fitness of final population: '
print parents.get_scores()
print 'final mstep: ', mstep, 2 * '\n'
#-------------------------------------------------------------------------------
#--- part 4: making a plot showing ---------------------------------------------
#--- a) fitness over time and --------------------------------------------------
#--- b) step size over time ----------------------------------------------------
#-------------------------------------------------------------------------------
N = len(s_rec)
g_rec = np.array(g_rec) - 0.4 + 0.8 * npr.rand(
N) # a little random horizontal whiggeling for better visibility
l1 = plt.plot(g_rec, s_rec, 'bo', label='fitness')
