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3jugs2.py
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3jugs2.py
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#File 3JugsGA.py
#Algoritmo para resolver el problema de las 3 jarras
#Miguel Angel Maya Hernandez
#Last change: 23 de Septiembre 2016
#Este algoritmo considera a todo el cromosoma como la mejor solucion posible
#, considerando el que tenga menor numero de operaciones no vacias
# -- Tabla de movimientos validos --
# 0 Llenar Jarra 8L
# 1 Llenar Jarra 5L
# 2 Llenar Jarra 3L
# 3 Tirar Jarra 8L
# 4 Tirar Jarra 5L
# 5 Tirar Jarra 3L
# 6 Mover 8L a 5L
# 7 Mover 8L a 3L
# 8 Mover 5L a 8L
# 9 Mover 5L a 3L
# 10 Mover 3L a 8L
# 11 Mover 3L a 5L
# 12 Hacer nada.
import math
import random
#Genera un cromosoma random con movimientos de la tabla
def random_chromosome():
chromosome=[]
for i in range(0,L_chromosome):
chromosome.append(random.randint(0,12))
return chromosome
#Regresa el valor de las tres jarras al final de los movimientos
#Regresa el valor de las tres jarras en su mejor momento
def decode_chromosome(chromosome):
global L_chromosome
j8=0
j5=0
j3=0
j1=0
j2=0
for p in range(0,L_chromosome):
if chromosome[p] == 0:
j8=8
elif chromosome[p] == 1:
j5=5
elif chromosome[p] == 2:
j3=3
elif chromosome[p] == 3:
j8=0
elif chromosome[p] == 4:
j5=0
elif chromosome[p] == 5:
j3=0
elif chromosome[p] == 6:
j1=j8 #a mover
j2=5-j5 #disponible
if j1 <= j2 :
j5+=j1
j8-=j1
else :
j5+=j2
j8-=j2
elif chromosome[p] == 7:
j1=j8 #a mover
j2=3-j3 #disponible
if j1 <= j2 :
j3+=j1
j8-=j1
else :
j3+=j2
j8-=j2
elif chromosome[p] == 8:
j1=j5 #a mover
j2=8-j8 #disponible
if j1 <= j2 :
j8+=j1
j5-=j1
else :
j8+=j2
j5-=j2
elif chromosome[p] == 9:
j1=j5 #a mover
j2=3-j3 #disponible
if j1 <= j2 :
j3+=j1
j5-=j1
else :
j3+=j2
j5-=j2
elif chromosome[p] == 10:
j1=j3 #a mover
j2=8-j8 #disponible
if j1 <= j2 :
j8+=j1
j3-=j1
else :
j8+=j2
j3-=j2
elif chromosome[p] == 11:
j1=j3 #a mover
j2=5-j5 #disponible
if j1 <= j2 :
j5+=j1
j3-=j1
else :
j5+=j2
j3-=j2
return (j8, j5, j3)
def f (j8, j5, j3):
res=0
res = min(math.fabs(buscado - j8), math.fabs(buscado - j5))
return res
def evaluate_chromosomes():
global F0
for p in range(N_chromosomes):
(j8, j5, j3)=decode_chromosome(F0[p])
fitness_values[p]=f(j8, j5, j3)
def cont_nulos(chromosome):
contador=0
for p in range(L_chromosome):
if chromosome[p]==12:
contador+=1
return contador
def compare_chromosomes(chromosome1,chromosome2):
(j8, j5, j3)=decode_chromosome(chromosome1)
fvc1=f(j8, j5, j3)
(j8, j5, j3)=decode_chromosome(chromosome2)
fvc2=f(j8, j5, j3)
if fvc1 > fvc2:
return 1
elif fvc1 == fvc2:
n1= cont_nulos(chromosome1)
n2= cont_nulos(chromosome2)
if n2 > n1 :
return 1
elif n2 == n1:
return 0
else :
return -1
return 0
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:"
(j8, j5, j3)=decode_chromosome(F0[0])
print "f(",j8, j5, j3,") = ", f(j8, j5, j3)," \n Chromosome- ",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)))]=random.randint(0,12);
if random.random() < prob_m:
o2[int(round(random.random()*(L_chromosome-1)))]=random.randint(0,12)
#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 F0
print "3 Jugs with GA \n"
buscado=4
L_chromosome = 50
prob_m=0.5
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(0,500):
nextgeneration()
print