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square.py
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square.py
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from Tkinter import *
import math
import random
def random_chromosome():
chromosome=[]
for i in range(0,L_chromosome):
if random.random()<0.5:
chromosome.append(0)
else:
chromosome.append(1)
return chromosome
def decode_float(lower, upper, bits):
global N_chains
Lbits=len(bits)
value=0
for p in range(Lbits):
value+=(2**p)*bits[-1-p]
return lower+(upper-lower)*float(value)/(N_chains-1)
#binary codification
def decode_chromosome(chromosome):
h=decode_float(0, 10, chromosome[:Nbits_h])
k=decode_float(0, 10, chromosome[Nbits_h:Nbits_h+Nbits_k])
l=decode_float(0,5, chromosome[Nbits_h+Nbits_k:Nbits_h+Nbits_k+Nbits_l])
return (h, k, l)
def f(x):
global points
(h,k,l)=x
TotalDistance=0
maxD=0
error=0
for index in range (len(points)):
TotalDistance+= (points[index][0]-h)**2 + (points[index][1]-k)**2
for index1 in range (len(points)):
for index2 in range(len(points)):
Dx=abs(points[index1][0] - points[index2][0])
Dy=abs(points[index1][1] - points[index2][1])
maxDaux=max(Dx, Dy)
if maxDaux > maxD:
maxD=maxDaux
error = TotalDistance + abs(maxD - l)
return error
def evaluate_chromosomes():
global F0
for p in range(N_chromosomes):
v=decode_chromosome(F0[p])
fitness_values[p]=f(v)
def compare_chromosomes(chromosome1,chromosome2):
vc1=decode_chromosome(chromosome1)
vc2=decode_chromosome(chromosome2)
fvc1=f(vc1)
fvc2=f(vc2)
if fvc1 > fvc2:
return 1
elif fvc1 == fvc2:
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
fraction[0]-=(sum(fraction)-1.0)/2
fraction[1]-=(sum(fraction)-1.0)/2
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():
w.delete(ALL)
F0.sort(cmp=compare_chromosomes)
print "Best solution so far:"
print "f(",decode_chromosome(F0[0]),")= ", f(decode_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)))]^=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
graph_population(F0,'red')
graph_population(F1,'green')
graph_f()
#The generation replaces the old one
F0[:]=F1[:]
def graph_f():
for index in range (len(points)):
w.create_oval(xo+points[index][0]*s, yo-points[index][1]*s, xo+(points[index][0]+.2 )*s, yo-(points[index][1]+.2)*s, fill="black")
def graph_population(F,color):
for chromosome in F:
h,k,l=decode_chromosome(chromosome)
w.create_rectangle( (h-l/2.0)*s, (yo-(k+l/2.0)*s), (h+l/2.0)*s , (yo-(k-l/2.0)*s) ,outline=color)
#Chromosomes are 4 bits long
points = [[5, 3], [5,7], [7,5]]
Nbits_l=Nbits_h=Nbits_k=8
L_chromosome=3*Nbits_l
N_chains=2**Nbits_l
crossover_point=L_chromosome/2
#Number of chromosomes
N_chromosomes=10
#probability of mutation
prob_m=0.9
F0=[]
fitness_values=[]
for i in range(0,N_chromosomes):
F0.append(random_chromosome())
fitness_values.append(0)
Lwheel=N_chromosomes*10
F1=F0[:]
#visualization
master = Tk()
xmax=200
ymax=200
xo=0
yo=200
s=20
w = Canvas(master, width=xmax, height=ymax)
w.pack()
button1 = Button(master, text="Next Generation", command=nextgeneration)
button1.pack()
graph_f()
graph_population(F0,'red')
F0.sort(cmp=compare_chromosomes)
evaluate_chromosomes()
mainloop()