def brachistochrone():
# create initial population
population = create_population(size_pop)
# evaluate population
population = [[indiv[:], fitness(start+indiv[:]+finish)] for indiv in population]
population.sort(key=itemgetter(1))
for generation in xrange(size_gen):
parents = []
# select parents
if seleccao==2: # tournament
for i in xrange(size_pop):
parents.extend([tournament(population[:],tsize)])
parents = [tournament(population[:], tsize) for i in xrange(size_pop)]
else: # roulette
sumfitness = 0.0
for i in population:
sumfitness += 1/i[1]
probability = [0 for i in xrange(size_pop)]
sumprob = 0.0
for i in xrange(size_pop):
probability[i] = sumprob + float((1.0/population[i][1]) / sumfitness)
sumprob += probability[i]
for i in xrange(size_pop):
parents.extend([roulette(population[:], probability)])
# produce offspring
offspring = []
# crossover
for i in xrange(0, size_pop,2):
if random.random() < prec:
offspring.extend(recnpoints(parents[i][:],parents[i+1][:]))
else:
offspring.extend([parents[i][:],parents[i+1][:]])
# mutation
for i in xrange(size_pop):
offspring[i] = mutation(offspring[i][:])
# evaluate offspring
offspring2 = [[indiv[0][:], fitness(start+indiv[0][:]+finish)] for indiv in offspring]
offspring2.sort(key=itemgetter(1))
# select survivors
for i in xrange(size_pop - elite):
population[size_pop-i-1] = offspring2[i][:]
population2 = [[indiv[0][:], fitness(start+indiv[0][:]+finish)] for indiv in population]
population = sorted(population2[:], key=itemgetter(1))
excelWriter.writerow(("Generation: ",str(generation+1), "Best: ",str(population[0][1]), "Worst: ", str(population[-1][1]), "Average: ", str(average(population)), "Desvio: ", str(stdev(population)) ))
if not generation%10:
print str(generation)+"\n"
FILE.write("Best Curve in Repetition "+str(repetition+1)+":\n"+str(population[0])+"\n\n")
return True
def brachistochrone():
# create initial population
population = create_population(size_pop)
# evaluate population
population = [[indiv[:], fitness(start+indiv[:]+finish)] for indiv in population]
population.sort(key=itemgetter(1))
for generation in xrange(size_gen):
parents = []
# select parents
if seleccao==2: # tournament
for i in xrange(size_pop):
parents.extend([tournament(population[:],tsize)])
parents = [tournament(population[:], tsize) for i in xrange(size_pop)]
else: # roulette
sumfitness = 0.0
for i in population:
sumfitness += 1/i[1]
probability = [0 for i in xrange(size_pop)]
sumprob = 0.0
for i in xrange(size_pop):
probability[i] = sumprob + float((1.0/population[i][1]) / sumfitness)
sumprob += probability[i]
parents = [roulette(population[:], probability) for i in xrange(size_pop)]
# produce offspring
offspring = []
# crossover
for i in xrange(0, size_pop,2):
if random.random() < prec:
offspring.extend(recnpoints(parents[i][:],parents[i+1][:]))
else:
offspring.extend([parents[i][:],parents[i+1][:]])
# mutation
for i in xrange(size_pop):
offspring[i] = mutation(offspring[i][:])
# evaluate offspring
offspring2 = [[indiv[0][:], fitness(start+indiv[0][:]+finish)] for indiv in offspring]
offspring2.sort(key=itemgetter(1))
# select survivors
for i in xrange(size_pop - elite):
population[size_pop-i-1] = offspring2[i][:]
population2 = [[indiv[0][:], fitness(start+indiv[0][:]+finish)] for indiv in population]
population = sorted(population2[:], key=itemgetter(1))
excelWriter.writerow(("Generation: ",str(generation+1), "Best: ",str(population[0][1]), "Worst: ", str(population[-1][1]), "Average: ", str(average(population)), "Desvio: ", str(stdev(population)) ))
if not generation%10:
print str(generation)+"\n"
FILE.write("Best Curve in Repetition "+str(repetition+1)+":\n"+str(population[0])+"\n\n")
return True
def drawCurve(rTurtle, points, xScale, yScale):
rTurtle.ht()
rTurtle.clear()
rTurtle.pu()
rTurtle.setpos(
start[0] * xScale - (canvasWidth / 2) - start[0] * xScale,
start[1] * yScale - (canvasHeight / 2) - finish[1] * yScale + 100,
)
rTurtle.pd()
rTurtle.color("black")
for i in xrange(0, len(points)):
rTurtle.goto(points[i])
rTurtle.dot()
rTurtle.pu()
rTurtle.setpos(
start[0] * xScale - (canvasWidth / 2) - start[0] * xScale,
start[1] * yScale - (canvasHeight / 2) - finish[1] * yScale + 100,
)
points, bestpoints = brachistochroneReal(start[0], start[1], finish[0], finish[1], n_points + 2)
rTurtle.pd()
rTurtle.color("blue")
for i in xrange(0, len(points)):
rTurtle.goto(
points[i][0] * xScale - (canvasWidth / 2) - start[0] * xScale,
points[i][1] * yScale - (canvasHeight / 2) - finish[1] * yScale + 100,
)
rTurtle.dot()
rTurtle.pu()
best_fit = fitness(bestpoints)
print "Real best: %f seconds " % best_fit
def drawCurve( rTurtle, points, xScale, yScale):
rTurtle.ht()
rTurtle.clear()
rTurtle.pu()
rTurtle.setpos(start[0]*xScale-(canvasWidth/2)-start[0]*xScale, start[1] *yScale-(canvasHeight/2)-finish[1]*yScale+100)
rTurtle.pd()
rTurtle.color("black")
for i in xrange(0,len(points)):
rTurtle.goto(points[i])
rTurtle.dot()
rTurtle.pu()
rTurtle.setpos(start[0]*xScale-(canvasWidth/2)-start[0]*xScale, start[1] *yScale-(canvasHeight/2)-finish[1]*yScale+100)
points, bestpoints = brachistochroneReal(start[0], start[1], finish[0], finish[1], n_points+2)
rTurtle.pd()
rTurtle.color("blue")
for i in xrange(0, len(points)):
rTurtle.goto(points[i][0]*xScale-(canvasWidth/2)-start[0]*xScale, points[i][1]*yScale-(canvasHeight/2)-finish[1]*yScale+100)
rTurtle.dot()
rTurtle.pu()
best_fit = fitness(bestpoints)
print "Real best: %f seconds " %best_fit
# select survivors
for i in xrange(size_pop - elite):
population[size_pop-i-1] = offspring2[i][:]
population2 = [[indiv[0][:], fitness(start+indiv[0][:]+finish)] for indiv in population]
population = sorted(population2[:], key=itemgetter(1))
excelWriter.writerow(("Generation: ",str(generation+1), "Best: ",str(population[0][1]), "Worst: ", str(population[-1][1]), "Average: ", str(average(population)), "Desvio: ", str(stdev(population)) ))
if not generation%10:
print str(generation)+"\n"
FILE.write("Best Curve in Repetition "+str(repetition+1)+":\n"+str(population[0])+"\n\n")
return True
points, bestpoints = brachistochroneReal(start[0], start[1], finish[0], finish[1], n_points+2)
bestRealFitness = fitness(bestpoints)
if representacao:
representacaoStr = "X fixos"
else:
representacaoStr = "S/ X fixos"
if seleccao == 2:
seleccaoStr = "Torneio"
else:
seleccaoStr = "Roleta"
sizes_pop = [50,100,150,200,300,400,500]
for size_pop in sizes_pop:
FILE = open("results-size_pop_"+str(size_pop)+"-best.txt",'w')
excelWriter.writerow(
("Generation: ", str(generation + 1), "Best: ",
str(population[0][1]), "Worst: ", str(population[-1][1]),
"Average: ", str(average(population)), "Desvio: ",
str(stdev(population))))
if not generation % 10:
print str(generation) + "\n"
FILE.write("Best Curve in Repetition " + str(repetition + 1) + ":\n" +
str(population[0]) + "\n\n")
return True
points, bestpoints = brachistochroneReal(start[0], start[1], finish[0],
finish[1], n_points + 2)
bestRealFitness = fitness(bestpoints)
if representacao:
representacaoStr = "X fixos"
else:
representacaoStr = "S/ X fixos"
if seleccao == 2:
seleccaoStr = "Torneio"
else:
seleccaoStr = "Roleta"
recombinations = [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
rec_points = 5
for prec in recombinations:
def brachistochrone(rTurtle):
# create initial population
population = create_population(size_pop)
# evaluate population
population = [[indiv[:], fitness(start + indiv[:] + finish)] for indiv in population]
population.sort(key=itemgetter(1))
for generation in xrange(size_gen):
parents = []
# select parents
if seleccao == 2: # tournament
for i in xrange(size_pop):
parents.extend([tournament(population[:], tsize)])
parents = [tournament(population[:], tsize) for i in xrange(size_pop)]
else: # roulette
sumfitness = 0.0
for i in population:
sumfitness += 1.0 / i[1]
probability = [0 for i in xrange(size_pop)]
sumprob = 0.0
for i in xrange(size_pop):
probability[i] = sumprob + float((1.0 / population[i][1]) / sumfitness)
sumprob += probability[i]
for i in xrange(size_pop):
parents.extend([roulette(population[:], probability)])
# produce offspring
offspring = []
# crossover
for i in xrange(0, size_pop, 2):
if random.random() < prec:
offspring.extend(recnpoints(parents[i][:], parents[i + 1][:]))
else:
offspring.extend([parents[i][:], parents[i + 1][:]])
# mutation
for i in xrange(size_pop):
offspring[i] = mutation(offspring[i][:])
# evaluate offspring
offspring2 = [[indiv[0][:], fitness(start + indiv[0][:] + finish)] for indiv in offspring]
offspring2.sort(key=itemgetter(1))
# select survivors
for i in xrange(size_pop - elite):
population[size_pop - i - 1] = offspring2[i][:]
population2 = [[indiv[0][:], fitness(start + indiv[0][:] + finish)] for indiv in population]
population = sorted(population2[:], key=itemgetter(1))
FILE.write("Generation: " + str(generation + 1) + "\n\n")
FILE.write("Best: " + str(population[0][1]) + " seconds\n")
FILE.write("Worst: " + str(population[-1][1]) + " seconds\n")
FILE.write("Average: " + str(average(population)) + " seconds\n")
FILE.write("Standard Deviation: " + str(stdev(population)) + " seconds\n")
FILE.write("-" * 30 + "\n")
print str(generation) + "\n"
points = []
xScale = canvasWidth / (finish[0] - start[0])
yScale = canvasHeight / (start[1] - finish[1])
for i in xrange(0, len(population[0][0]), 2):
points.append(
[
population[0][0][i] * xScale - (canvasWidth / 2) - start[0] * xScale,
population[0][0][i + 1] * yScale - (canvasHeight / 2) - finish[1] * yScale + 100,
]
)
points.append(
[
finish[0] * xScale - (canvasWidth / 2) - start[0] * xScale,
finish[1] * yScale - (canvasHeight / 2) - finish[1] * yScale + 100,
]
)
drawCurve(rTurtle, points, xScale, yScale)
print "Best took %f seconds " % population[0][1]
best.append(population[0][1])
return True
def brachistochrone( rTurtle):
# create initial population
population = create_population(size_pop)
# evaluate population
population = [[indiv[:], fitness(start+indiv[:]+finish)] for indiv in population]
population.sort(key=itemgetter(1))
for generation in xrange(size_gen):
parents = []
# select parents
if seleccao==2: # tournament
for i in xrange(size_pop):
parents.extend([tournament(population[:],tsize)])
parents = [tournament(population[:], tsize) for i in xrange(size_pop)]
else: # roulette
sumfitness = 0.0
for i in population:
sumfitness += 1.0/i[1]
probability = [0 for i in xrange(size_pop)]
sumprob = 0.0
for i in xrange(size_pop):
probability[i] = sumprob + float((1.0/population[i][1]) / sumfitness)
sumprob += probability[i]
for i in xrange(size_pop):
parents.extend([roulette(population[:], probability)])
# produce offspring
offspring = []
# crossover
for i in xrange(0, size_pop,2):
if random.random() < prec:
offspring.extend(recnpoints(parents[i][:],parents[i+1][:]))
else:
offspring.extend([parents[i][:],parents[i+1][:]])
# mutation
for i in xrange(size_pop):
offspring[i] = mutation(offspring[i][:])
# evaluate offspring
offspring2 = [[indiv[0][:], fitness(start+indiv[0][:]+finish)] for indiv in offspring]
offspring2.sort(key=itemgetter(1))
# select survivors
for i in xrange(size_pop - elite):
population[size_pop-i-1] = offspring2[i][:]
population2 = [[indiv[0][:], fitness(start+indiv[0][:]+finish)] for indiv in population]
population = sorted(population2[:], key=itemgetter(1))
FILE.write("Generation: "+str(generation+1)+"\n\n")
FILE.write("Best: "+str(population[0][1])+" seconds\n")
FILE.write("Worst: "+str(population[-1][1])+" seconds\n")
FILE.write("Average: "+str(average(population))+" seconds\n")
FILE.write("Standard Deviation: "+str(stdev(population))+" seconds\n")
FILE.write("-"*30+"\n")
print str(generation)+"\n"
points = []
xScale = canvasWidth/(finish[0] - start[0])
yScale = canvasHeight/(start[1] - finish[1])
for i in xrange(0,len(population[0][0]),2):
points.append([population[0][0][i]*xScale-(canvasWidth/2)-start[0]*xScale , population[0][0][i+1]*yScale-(canvasHeight/2)-finish[1]*yScale +100])
points.append([finish[0]*xScale-(canvasWidth/2)-start[0]*xScale, finish[1]*yScale-(canvasHeight/2)-finish[1]*yScale + 100])
drawCurve(rTurtle, points,xScale, yScale)
print "Best took %f seconds " %population[0][1]
best.append(population[0][1])
return True
