def test_adaptive_crossover(self):
# Set up for test
self.gen.makep()
if self.gen.g == 1:
self.gen.q = binsel(self.gen.p, self.gen.pop, 'elitism')
else:
self.q = binsel(self.gen.p, self.gen.pop, 'cco')
# Run adaptive crossover
self.gen.adaptive_crossover()
self.assertEqual(
len(self.gen.q) + len(self.gen.newgenes), self.gen.pop)
def rungen(self):
self.t += 1
if self.t % 50 == 0:
gent = datetime.datetime.now()
print(' ', gent)
print('t = ', self.t)
self.makep()
if self.t == 1:
self.q = ga.binsel(self.p, self.pop, 'elitism')
else:
self.q = ga.binsel(self.p, self.pop, 'cco')
self.newgenes = ga.xover(self.q, self.pop, 0.9)
self.newgenes = ga.mutat(self.n, self.newgenes, self.pop)
self.makeq()
def rungen(self):
self.update_gen()
# Create the parent pool:
self.makep()
if self.g == 1:
self.q = ga.binsel(self.p, self.pop, 'elitism')
else:
self.q = ga.binsel(self.p, self.pop, 'cco')
self.adaptive_crossover()
# Mutation random or selected
self.q, self.newgenes = \
ga.mutat_mv(self.q, self.newgenes, self.pop, self.prob_mutat)
self.fill_q()
# Adaptive Local search
self.local_search()
self.cleanarchive()
print(self.funkeval, 'function evaluations have been performed.\n')
def rungen(self):
self.g += 1
# Print out generation info:
if self.g % 50 == 0:
gent = datetime.now()
print(' ', gent)
print(' ')
print('gen = ', self.g)
# Create the parent pool:
self.makep()
if self.g == 1:
self.q = ga.binsel(self.p, self.pop, 'elitism')
else:
self.q = ga.binsel(self.p, self.pop, 'cco')
# Use the mixed-variable specific modules:
self.q, self.newgenes = ga.xover_mv(self.q, self.pop, 0.9)
self.q, self.newgenes = ga.mutat_mv(self.q, self.newgenes, self.pop,
0.3)
self.makeq()
print(self.funkeval, 'function evaluations have been performed.\n')
def rungen(self):
self.t += 1
#if self.t % 50 == 0:
gent = datetime.now()
print(' ', gent)
print('t = ', self.t)
print('Selecting parent population...')
self.makep()
print('GA operation: binary selection...')
if self.t == 1:
self.q = ga.binsel(self.p, self.pop, 'elitism')
else:
self.q = ga.binsel(self.p, self.pop, 'cco')
print('GA operation: crossover...')
self.newgenes = ga.xover(self.q, self.pop, 0.8)
print('GA operation: mutation...')
self.evoloperators()
if self.t % 10 == 0:
self.localsearch()
print(self.funkeval, 'function evaluations have been performed.\n')
def soga(self, k, mems, numgen):
# Initialize single-objective solutions using genetic algorithm
q = []
newgenes = []
for t in range(numgen):
print(' Subgeneration', t)
p, newgenes = self.makesop(t, mems, q, newgenes)
if t != 0:
q = ga.binsel(p, mems, 'elitism')
newgenes = ga.xover(q, mems, 0.9)
newgenes = ga.mutat(self.n, newgenes, mems)
q = self.makesoq(t, mems, k, p, q, newgenes)
q = sorank(q)
return q[0]