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gautil.py
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gautil.py
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import model
import itertools
import numpy
import random
import bisect
import minmin
from chromosome import Chromosome , crossover
P_SIZE=200 # population size
CROSSOVER_PRB=0.6 # crossover probability
MUTATION_PRB=0.4 # mutation probability
DEBUG = False
# Generate initial population. We seed it with one min-min value
def generate_new_population(M, seed=True):
chrs=[]
if seed:
# Seeding one chromosome with one
# minmin chromosome
for _ in itertools.repeat(None, P_SIZE-1):
chrs.append(Chromosome(M))
# Seed min-min
ch = Chromosome(M, tabu_list=None, empty=True)
mapping = minmin.run(M)
if DEBUG: print mapping, mapping.makespan()
for t in range(mapping._model.ntasks):
ch.map.assign(t, mapping.machine(t))
chrs.append(ch)
print "Seeded chromosome value:", ch.value()
else:
# No seeding happens here
for _ in itertools.repeat(None, P_SIZE):
chrs.append(Chromosome(M))
return chrs
'''
def calc_probability(chrs):
total = 0
result=[]
for ch in chrs:
total+=ch.value()
for ch in chrs:
# lowest value should have higher probability
# this normalization is broken
result.append(ch.value()/total)
assert len(result) == len(chrs)
return result
'''
def calc_probability(chrs):
total = 0
rtotal = 0
result=[]
for ch in chrs:
total+=ch.value()
for ch in chrs:
# lowest value should have higher probability
# this normalization is broken
result.append(total/ch.value())
rtotal+=total/ch.value()
for i in range(len(result)):
result[i] = result[i]/rtotal
assert len(result) == len(chrs)
return result
def find_elite_index(prb):
return numpy.argmax(numpy.array(prb))
def find_elite_ch(chrs, prb=None):
if (None != prb):
assert len(prb) == len(chrs)
return (chrs[find_elite_index(prb)],
find_elite_index(prb))
else:
# if now prb was provided we have todo
# manual search
minv=-1
min_index=-1
for i in range(len(chrs)):
if i == 0 or chrs[i].value() < minv :
minv = chrs[i].value()
min_index = i
return (chrs[min_index], min_index)
def select_with_probability(num, prb, chrs):
assert len(prb) == len(chrs)
acc_prb = [None]*len(prb)
acc_prb[0] = prb[0]
for i in range(1,len(prb)):
acc_prb[i]=acc_prb[i-1] + prb[i]
# the rest of the selection code is based on
# http://stackoverflow.com/questions/4113307/pythonic-way-to-select-list-elements-with-different-probability
result=[]
for _ in range(num):
x = random.random()
index = bisect.bisect(acc_prb, x)
result.append(chrs[index])
return result
def select_for_crossover(chrs):
# Select even number of chromosome for crossover
result = []
for ch in chrs:
if random.random() <= CROSSOVER_PRB:
result.append(ch)
if (len(result)/2):
result.pop()
return result
def mutate_chromosome(ch):
if random.random() <= MUTATION_PRB:
ch.apply_mutatation()
if DEBUG: print "M: ", ch
''' Not used
def mutate_population(chrs):
for ch in len(chrs):
if random.random() <= MUTATION_PRB:
ch.apply_mutatation()
'''
def crossover_population(M, chrs):
c_ppl = select_for_crossover(chrs)
for _ in c_ppl:
a = c_ppl.pop()
b = c_ppl.pop()
c = crossover(M, a, b)
if DEBUG: print "A: ", a
if DEBUG: print "B: ", b
if DEBUG: print "C: ", c
mutate_chromosome(c)
chrs.append(c)
# GSA related code
TEMP_ADJ = 0.9 # 10% for each step
def calc_temp(chrs):
size = len(chrs)
total = 0
for ch in chrs:
total+=ch.value()
return total/size # average
def adjust_temp(temp):
return temp*TEMP_ADJ
def gsacrossover_population(M, chrs, temp):
c_ppl = select_for_crossover(chrs)
for _ in c_ppl:
minch = maxch = None
a = c_ppl.pop()
b = c_ppl.pop()
c = crossover(M, a, b)
if DEBUG: print "A: ", a
if DEBUG: print "B: ", b
if DEBUG: print "C: ", c
mutate_chromosome(c)
a_val=a.value()
b_val=b.value()
c_val=c.value()
if (a_val > b_val):
maxch = a
minch = b
else:
maxch = b
minch = a
if c_val < (maxch.value() + temp) :
# accept mutant to new population
chrs.append(c)
chrs.remove(maxch)
# otherwise we don't accept the mutant to
# the new population
assert len(chrs) == P_SIZE-1