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coinChangeGA.py
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coinChangeGA.py
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#File coinChangeAG.py
#Algoritmo para resolver coin Change Genetic Algorithm
#Miguel Angel Maya Hernandez
#Last change: 23 de Septiembre 2016
import math
import random
def random_chromosome():
chromosome=[]
for i in range(0,L_chromosome):
if random.random()<0.1:
chromosome.append(0)
else:
chromosome.append(1)
return chromosome
#binary codification
def decode_chromosome(chromosome):
global L_chromosome,Di,tam
amount=0
tcant=0
cantidad=0
i=0
j=0
#print chromosome
for p in range(0,L_chromosome):
cantidad += 2**i * chromosome[-1-p]
i+=1
if i % tam == 0 and p > 0:
amount+=Di[j]*cantidad
tcant+=cantidad
cantidad = 0
i = 0
j += 1
return (amount, tcant)
def f (amount):
global conversion
res = 0
if amount > conversion:
res-= amount-conversion
else:
res=amount
return res
def evaluate_chromosomes():
global F0
for p in range(N_chromosomes):
#print F0[p]
(am, cant)=decode_chromosome(F0[p])
#print pe," ",ga
fitness_values[p]=f(am)
def compare_chromosomes(chromosome1,chromosome2):
(am1, cant1)=decode_chromosome(chromosome1)
(am2, cant2)=decode_chromosome(chromosome2)
fvc1=f(am1)
fvc2=f(am2)
if fvc1 > fvc2:
return -1
elif fvc1 == fvc2:
if cant1 > cant2:
return 1
elif cant1 == cant2:
return 0
else:
return -1
else: #fvg1<fvg2
return 1
def create_wheel():
global F0,fitness_values
maxv=max(fitness_values)
acc=0
for p in range(N_chromosomes):
acc+=maxv-fitness_values[p]
fraction=[]
for p in range(N_chromosomes):
fraction.append( float(maxv-fitness_values[p])/acc)
if fraction[-1]<=1.0/Lwheel:
fraction[-1]=1.0/Lwheel
## print fraction
fraction[0]-=(sum(fraction)-1.0)/2
fraction[1]-=(sum(fraction)-1.0)/2
## print fraction
wheel=[]
pc=0
for f in fraction:
Np=int(f*Lwheel)
for i in range(Np):
wheel.append(pc)
pc+=1
return wheel
def nextgeneration():
global n_generation
F0.sort(cmp=compare_chromosomes)
n_generation+=1
print "Generation: ",n_generation
print "Best solution so far:"
(v1,am)=decode_chromosome(F0[0])
print "f(",v1,") = ", f(v1)," # con ", am," monedas -",F0[0]
#elitism, the two best chromosomes go directly to the next generation
F1[0]=F0[0]
F1[1]=F0[1]
for i in range(0,(N_chromosomes-2)/2):
roulette=create_wheel()
#Two parents are selected
p1=random.choice(roulette)
p2=random.choice(roulette)
#Two descendants are generated
o1=F0[p1][0:crossover_point]
o1.extend(F0[p2][crossover_point:L_chromosome])
o2=F0[p2][0:crossover_point]
o2.extend(F0[p1][crossover_point:L_chromosome])
#Each descendant is mutated with probability prob_m
if random.random() < prob_m:
o1[int(round(random.random()*(L_chromosome-1)))]^=1
if random.random() < prob_m:
o2[int(round(random.random()*(L_chromosome-1)))]^=1
#The descendants are added to F1
F1[2+2*i]=o1
F1[3+2*i]=o2
#The generation replaces the old one
F0[:]=F1[:]
print "Coin Change with GA \n"
Di = map(int, raw_input("Denominaciones de moneda (iniciar con 1)\n").split())
conversion = int(raw_input("Dinero a cambiar\n"))
tam = int(math.ceil(math.log(conversion, 2)))
L_chromosome = len(Di)*tam
prob_m=0.7
crossover_point=L_chromosome/2
N_chromosomes=10
F0=[]
F1=[]
fitness_values=[]
for i in range(0,N_chromosomes):
F0.append(random_chromosome())
fitness_values.append(0)
Lwheel=N_chromosomes*10
F1=F0[:]
F0.sort(cmp=compare_chromosomes)
evaluate_chromosomes()
n_generation=0
print "\n"
for i in range(0,200):
nextgeneration()
print "\n"