Max,
def GeneticNextGen(self):
numsets = len(self.WtSets)
numwts = len(self.WtSets[0].Lis)
self.WtSets.sort(CompByCurrentFitness)
index_lis = []
K = 100.0
N = float(numwts)
#if RISE(slope) is too high, concentration occurs too fast and
#you lose many quickly
RISE = -0.01*K
RUN = N - 1.0
m = RISE/RUN
for i in range( numsets ):
x = float(i)
numin = int(m * x + K)
for k in range(numin):
index_lis.append( i )
new_wtset_list = WtSetListClass()
while len(new_wtset_list.WtSets) < numsets:
#split in a number of placeses
splitPoints = [] #empty list of
places where dna's are crossed
numSplitPoints = random.randint( 2, 4 ) #number of places
to cross at(not to hot & not to cold)
while len(splitPoints) < numSplitPoints: #get required num
of points at random
split_pt = random.randint( 0, numwts - 1 )
if split_pt not in splitPoints:
splitPoints.append(split_pt)
i1 = random.choice( index_lis ) #get two old
weight sets at random from a biased list
while( 1 ):
i2 = random.choice( index_lis )
if i2 <> i1:
break
wts1 = self.WtSets[ i1 ]
wts2 = self.WtSets[ i2 ]
list1 = wts1.Lis[0:] #just size new
weight sets
list2 = wts1.Lis[0:]
flip = False #copy into new
weight sets from old alternating the
for k in range(len(wts1.Lis)): # the source on 2
to 4 flip points
if k in splitPoints:
flip = not flip
if flip:
list1[k] = wts2.Lis[k]
list2[k] = wts1.Lis[k]
else:
list1[k] = wts1.Lis[k]
list2[k] = wts2.Lis[k]
split_pt1 = random.choice(splitPoints) #capture a place
to mutate at low probabilty
x = random.randint( 0, 1000 ) #.1 % of time mutate at boundry
if x == 5:
list1[ split_pt1 ] = RandomFloat(LOWWT,HIGHWT)
list2[ split_pt1 ] = RandomFloat(LOWWT,HIGHWT)
wt = WtSetClass( list1 )
wt.FoldParentFitnesses( wts1,wts2 )
new_wtset_list.WtSets.append( wt )
if len(new_wtset_list.WtSets) < numsets:
wt = WtSetClass( list2 )
wt.FoldParentFitnesses( wts1,wts2 )
new_wtset_list.WtSets.append( wt )
x = random.randint(0,10000)
if x == 5:
new_wtset_list.RandomizeRandomWt() #0.01% of time made an entire
new random wt
self.WtSets = new_wtset_list.WtSets
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