def main():
prob = problem.Problem(dim=3, prangetup=(-3, 3))
popu = Population(prob, size=10)
popu.randominit()
print(popu.poparr)
print(popu.avg_fitness())
print(popu.fitarr)
print(popu.find_expecarr())
print("problem starts here")
newsel = misc.Selection()
newcros = misc.Crossover()
newmuta = misc.Mutation()
for i in newsel.select_parent(popu):
child1, child2 = newcros.do_crossover(i)
child1 = newmuta.mutate(
child1,
prob.rangetup,
switch=True,
)
child2 = newmuta.mutate(child2, prob.rangetup)
print(child1, child2)
def main():
popsize = 1100
prob = problem.Problem(fitness_func=problem.katsuura,
dim=5,
prangetup=(-100, 100))
popu = population.Population(prob, size=popsize)
popu.randominit()
genlim = 2000
print(popu.poparr)
print("thing starts here")
newsel = misc.Selection(1)
newcros = misc.Crossover(rate=0.9)
newmuta = misc.Mutation(rate=0.1)
newterm = misc.Termination(1)
print(popu.avg_fitness())
for i in range(genlim):
lis = []
if (i == 200):
print("here it is-----------------------------------------")
if (i == 500):
print("here it is 500 ------------------------------------")
for tup in newsel.select_parent(popu):
child1, child2 = newcros.do_crossover(tup)
child1 = newmuta.mutate(child1,
prob.rangetup,
switch=1,
iteri=i,
switchiter=100,
factup=(100, 1000))
child2 = newmuta.mutate(child2,
prob.rangetup,
switch=1,
iteri=i,
switchiter=100,
factup=(100, 1000))
lis.append(child1)
lis.append(child2)
del (popu)
arr = np.array(lis)
popu = population.Population(prob, poparr=arr, size=popsize)
popu.set_fitarr()
if np.all(popu.poparr == popu.poparr[0]):
break #these two conditionals have pen-ultimate IMPORTANCE, as my normalization fails heavily if all are same
if np.all(popu.fitarr == popu.fitarr[0]):
break
print(popu.avg_fitness())
if newterm.terminate(popul=popu, iteri=i, lim=500):
print("breaking bad")
break
print(popu.poparr)
print(popu.avg_fitness())
def mutachange(nowavg,
prevavg,
prevtoprevavg,
mrate,
step=0.01): # so far programmed for minimization only
if (prevavg - prevtoprevavg) > step and (nowavg - prevavg) > step:
print("to next level")
mrate = mrate / 10
newmuta = misc.Mutation(rate=mrate)
prevtoprevavg = 2
prevavg = 1
nowavg = 0
return (newmuta, nowavg, prevavg, prevtoprevavg, mrate)
return None
def main():
prob = problem.Problem(prob_func=po.problem1_func,
randomvec_func=po.problem1_randomvec_func,
constraints=po.constraints,
dim=5,
prangetup=(-100, 100))
popu = Population(prob, size=10)
popu.randominit()
print("problem starts here")
newsel = misc.Selection(1)
newcros = misc.Crossover()
newmuta = misc.Mutation()
"""par1,par2=popu.poparr[0],popu.poparr[1]
print(par1,par2)
for i in range(100):
child1,child2=newcros.do_crossover((par1,par2))
child1=newmuta.mutate(child1,prob.constraints,switch=True,iteri=i)
child2=newmuta.mutate(child2,prob.constraints,iteri=i)
par1,par2=child1,child2
print(child1,child2)
"""
count = 0
for i in newsel.select_parent(popu):
child1, child2 = newcros.do_crossover(i)
child1 = newmuta.mutate(child1,
prob.constraints,
switch=True,
iteri=count)
child2 = newmuta.mutate(child2, prob.constraints, iteri=count)
print(child1, child2)
count += 1
def main():
popsize = 1100
mrate = 0.1
minavg = np.inf
prob = problem.Problem(fitness_func=problem.rastrigin,
dim=10,
prangetup=(-100, 100))
popu = population.Population(prob, size=popsize)
popu.randominit()
genlim = 20000
print(popu.poparr)
print("thing starts here")
newsel = misc.Selection(1)
newcros = misc.Crossover(rate=0.9)
newterm = misc.Termination(1)
prevtoprevavg = 2
prevavg = 1
nowavg = 0
flag = 0
newmuta = misc.Mutation(rate=mrate)
print(mrate, "starts")
print(popu.avg_fitness())
for i in range(genlim):
lis = []
if (i == 200):
print("here it is-----------------------------------------")
if (i == 500):
print("here it is 500 ------------------------------------")
tup = mutachange(nowavg, prevavg, prevtoprevavg, mrate, step=0.001)
if tup:
(newmuta, nowavg, prevavg, prevtoprevavg, mrate) = tup
kickchange(popu,
nowavg,
prevavg,
prevtoprevavg,
kickrate=0.05,
step=0.001) #this changes population array
for tup in newsel.select_parent(popu):
child1, child2 = newcros.do_crossover(tup)
child1 = newmuta.mutate(child1,
prob.rangetup,
switch=1,
iteri=i,
switchiter=100,
factup=(100, 1000))
child2 = newmuta.mutate(child2,
prob.rangetup,
switch=1,
iteri=i,
switchiter=100,
factup=(100, 1000))
lis.append(child1)
lis.append(child2)
del (popu)
arr = np.array(lis)
popu = population.Population(prob, poparr=arr, size=popsize)
popu.set_fitarr()
if np.all(popu.poparr == popu.poparr[0]):
break #these two conditionals have pen-ultimate IMPORTANCE, as my normalization fails heavily if all are same
if np.all(popu.fitarr == popu.fitarr[0]):
break
print(popu.avg_fitness(), mrate, i)
if newterm.terminate(popul=popu, iteri=i, lim=500):
print("breaking bad")
print("for mrate ", mrate, " last gen avg is", popu.avg_fitness())
flag = 1
break
prevtoprevavg = prevavg
prevavg = nowavg
nowavg = popu.avg_fitness()
print(popu.poparr)
print(popu.avg_fitness())
def main():
mini = np.inf
popsize = 1100
prob = problem.Problem(prob_func=po.problem1_func,
randomvec_func=po.problem1_randomvec_func,
constraints=po.constraints,
dim=3,
prangetup=(-100, 100))
popu = population.Population(prob, size=popsize)
popu.randominit()
genlim = 200
#print(popu.poparr)
#print("thing starts here")
newsel = misc.Selection(1)
newcros = misc.Crossover(rate=0.8)
newmuta = misc.Mutation(rate=0.1)
mrate = 0.2
newterm = misc.Termination(0)
#print(popu.avg_fitness())
for i in range(genlim):
lis = []
if (i == 200):
print("here it is-----------------------------------------")
if (i == 5000):
print("here it is 500 ------------------------------------")
mrate = 0.02
newmuta = misc.Mutation(rate=0.01)
#popu.set_fitarr()
#minic=min(popu.fitarr)
for j in range(popu.size):
if popu.poparr[j][0] < 0:
print("reporting issue -ve")
exit()
for tup in newsel.select_parent(popu):
child1, child2 = newcros.do_crossover(tup)
if child1[0] < 0:
print("anarth h here")
child1 = newmuta.mutate(child1, prob.constraints)
child2 = newmuta.mutate(child2, prob.constraints)
for p in range(popu.size):
if popu.poparr[p][0] < 0:
print("reporting issue -ve")
exit()
lis.append(child1)
lis.append(child2)
del (popu)
#print("below here")
arr = np.array(lis)
popu = population.Population(prob, poparr=arr, size=popsize)
#print("below here")
popu.set_fitarr()
if np.all(popu.poparr == popu.poparr[0]):
break #these two conditionals have pen-ultimate IMPORTANCE, as my normalization fails heavily if all are same
if np.all(popu.fitarr == popu.fitarr[0]):
break
#print(popu.avg_fitness(),i)
#print(mini,list(mininp))
print(i, genlim)
if newterm.terminate(popul=popu, generationnum=i,
generationlim=genlim):
print("breaking bad")
break
print(popu.poparr)
