def go(self):
'''
'''
print 'Go go go'
jobs = []
for i in range(5, 100, 10):
jobs.append(
make_job(self.base_job,
{'name': '{}'.format(i),
'population': i,
'generations': 250,
'mutate': ('insert_mut', {'indpb': 0.05}),
}))
jobs.append(
make_job(self.base_job,
{'name': '{}m'.format(i),
'population': i,
'generations': 250,
'mutate': ('meta_insert', {'indpb': 0.05}),
}))
self.compare(jobs)
print 'Done'
def go(self):
'''
'''
print 'Go go go'
jobs = []
for pop in (100,):
jobs.append(
make_job(self.base_job,
{'name': '{}m'.format(pop),
'population': pop,
# 'generations': 1000,
'mutate': ('meta_insert', {'indpb': 0.05}),
'mate': 'cxSCX_meta',
}))
jobs.append(
make_job(self.base_job,
{'name': '{}'.format(pop),
'population': pop,
# 'generations': 1000,
'mutate': ('insert_mut', {'indpb': 0.05}),
'mate': 'cxSCX',
}))
self.compare(jobs)
print 'Done'
def go(self):
'''
'''
print 'Go go go'
jobs = []
for i in range(5, 100, 10):
jobs.append(
make_job(
self.base_job, {
'name': '{}'.format(i),
'population': i,
'generations': 250,
'mutate': ('insert_mut', {
'indpb': 0.05
}),
}))
jobs.append(
make_job(
self.base_job, {
'name': '{}m'.format(i),
'population': i,
'generations': 250,
'mutate': ('meta_insert', {
'indpb': 0.05
}),
}))
self.compare(jobs)
print 'Done'
def go(self):
self.t0 = time.time()
jobs = [make_job(self.base_job, {}),
make_job(self.base_job, {}),
make_job(self.base_job, {}),
]
self.launch(jobs, self.on_result)
print 'DONE'
def go(self):
self.t0 = time.time()
jobs = [
make_job(self.base_job, {}),
make_job(self.base_job, {}),
make_job(self.base_job, {}),
]
self.launch(jobs, self.on_result)
print 'DONE'
def go(self):
'''
'''
print 'Go go go'
jobs = []
for pop in (100, ):
jobs.append(
make_job(
self.base_job,
{
'name': '{}m'.format(pop),
'population': pop,
# 'generations': 1000,
'mutate': ('meta_insert', {
'indpb': 0.05
}),
'mate': 'cxSCX_meta',
}))
jobs.append(
make_job(
self.base_job,
{
'name': '{}'.format(pop),
'population': pop,
# 'generations': 1000,
'mutate': ('insert_mut', {
'indpb': 0.05
}),
'mate': 'cxSCX',
}))
self.compare(jobs)
print 'Done'
def go(self):
'''
Launches the super uber optimizer.
Generates a range of values for a parameter, selects
the best result and goes to the next parameter.
'''
print 'Go go go !'
# 2. Create dictionnaries for range
print 'Optimizing population'
jobs = []
for pop in range(1, 1000, 20):
job = make_job(self.base_job,
{'name': 'pop-{}'.format(pop),
'population': pop,
'generations': 10000 / pop,
})
jobs.append(job)
best_job = self.tournament(jobs)
print 'Optimizing cxpb'
jobs = []
for cxpb in xrange(1, 100, 5):
job = make_job(best_job,
{'name': '{}-{}'.format(best_job['operators']['name'], cxpb / 100.),
'cxpb': cxpb / 100.,
})
jobs.append(job)
best_job = self.tournament(jobs)
print 'Optimizing indpb'
jobs = []
for mut in range(1, 100, 5):
job = make_job(best_job,
{'name': '{}-{}'.format(job['name'], mut / 100.),
'mutate': (job['mutate'][0], {'indpb': mut / 100.}),
})
jobs.append(job)
best_job = self.tournament(jobs)
print 'Optimizing mutpb'
jobs = []
for mutpb in xrange(1, 100, 5):
job = make_job(best_job,
{'name': '{}-{}'.format(job['name'], mutpb / 100.),
'mutpb': mutpb / 100.,
})
jobs.append(job)
best_job = self.tournament(jobs)
print 'Optimizing crossover operator'
crossovers = ['cxPMX', 'cxSCX', 'cxERX', 'cxOX', 'cxHeuristic']
jobs = []
for cx_operator in crossovers:
job = make_job(best_job,
{'name': '{}-{}'.format(job['name'], cx_operator),
'mate': cx_operator,
})
if cx_operator == 'cxHeuristic':
job['evaluate'] = 'eval_adjacent'
job['mutate'] = ('mutshuf_adj', {'indpb': best_job['mutate'][1]})
job['indices'] = 'adj_creator'
else:
job['evaluate'] = 'eval_simple'
job['mutate'] = ('mutshuf', {'indpb': best_job['mutate'][1]})
job['indices'] = 'path_creator'
jobs.append(job)
best_job = self.tournament(jobs)
print 'Optimizing mutation operator'
jobs = []
if best_job['mate'] == 'cxHeuristic':
mutations = ['mutshuf_adj', 'invert_mut_adj', 'insert_mut_adj', 'simple_inv_adj']
else:
mutations = ['mutshuf', 'invert_mut', 'insert_mut', 'simple_inv']
for mut_operator in mutations:
job = make_job(best_job,
{'name': '{}-{}'.format(job['name'], mut_operator),
'mutate': (mut_operator, {'indpb': best_job['mutate'][1]}),
})
jobs.append(job)
best_job = self.tournament(jobs)
print 'runned'
raw_input()
