def get_correction_heurs(of, maxeval, FSA=True, GO=True):
overview_heurs = []
if FSA:
for T0 in [1e-10, 1e-2, 1, np.inf]:
for mutation in get_correction_mutation(of):
for n0 in [1, 2, 5]:
overview_heurs.append(
FastSimulatedAnnealing(of,
maxeval=maxeval,
T0=T0,
n0=n0,
alpha=2,
mutation=mutation))
if GO:
for mutation in get_correction_mutation(of):
for crossover in [UniformMultipoint(1)]:
for N in [100, 150, 200]:
for Tsel1 in [0.5]:
overview_heurs.append(
GeneticOptimization(of,
maxeval,
N=N,
M=N * 3,
Tsel1=Tsel1,
Tsel2=0.1,
mutation=mutation,
crossover=crossover))
return overview_heurs
def experiment_go(of, maxeval, num_runs, N, M, Tsel1, Tsel2, mutation, crossover):
results = []
heur_name = 'GO_{}'.format(N)
for i in tqdm(range(num_runs), 'Testing {}'.format(heur_name)):
result = GeneticOptimization(of, maxeval, N=N, M=M, Tsel1=Tsel1, Tsel2=Tsel2,
mutation=mutation, crossover=crossover).search()
result['run'] = i
result['heur'] = heur_name
result['N'] = N
results.append(result)
return pd.DataFrame(results, columns=['heur', 'run', 'N', 'best_x', 'best_y', 'neval'])
def get_overview_heurs(of, maxeval, RD=True, FSA=True, DE=True, GO=True):
overview_heurs = []
if RD:
for hmax in [0, 5, 10, 50, np.inf]:
for RD in [False, True]:
overview_heurs.append(
ShootAndGo(of,
maxeval=maxeval,
hmax=hmax,
random_descent=RD))
if FSA:
for T0 in [1e-10, 1e-2, 1, np.inf]:
for mutation in get_overview_mutation(of):
for n0 in [1, 2, 5]:
overview_heurs.append(
FastSimulatedAnnealing(of,
maxeval=maxeval,
T0=T0,
n0=n0,
alpha=2,
mutation=mutation))
if DE:
for N in [4, 10, 20]:
for CR in [0.2, 0.5, 0.8]:
for F in [0.5, 1, 2]:
overview_heurs.append(
DifferentialEvolution(of,
maxeval=maxeval,
N=N,
CR=CR,
F=F))
if GO:
for mutation in get_overview_mutation(of):
for crossover in [
Crossover(),
UniformMultipoint(1),
RandomCombination()
]:
for N in [1, 2, 5, 10, 30, 100]:
for Tsel1 in [0.5, 1]:
overview_heurs.append(
GeneticOptimization(of,
maxeval,
N=N,
M=N * 3,
Tsel1=Tsel1,
Tsel2=0.1,
mutation=mutation,
crossover=crossover))
return overview_heurs
def experiment_go(of, maxeval, num_runs, N):
results = []
heur_name = 'GO_{}'.format(N)
for i in range(num_runs):
result = GeneticOptimization(of, maxeval, N=N).search()
print('result ', result)
result['run'] = i
result['heur'] = heur_name
result['N'] = N
results.append(result)
# write results to csv file
now = datetime.now()
current_time = now.strftime('%Y%m%d%H%M')
res = pd.DataFrame(
results, columns=['heur', 'run', 'N', 'best_x', 'best_y', 'neval'])
res.to_csv('../results/' + current_time + '_one_eighth_GO_' + str(N) +
'.csv')
return res
mutation = {'mutation': LevyMutation(scale=parameter, correction = correction['correction']), 'name': 'levy{}_'
.format(parameter)+correction['name']}
mutations.append(mutation)
# In[8]:
results = pd.DataFrame()
for crossover in crossovers:
for mutation in mutations:
heur_name = 'GO_mut:({})_cro:{}'.format(mutation['name'], crossover['name'])
runs = []
for i in tqdm_notebook(range(NUM_RUNS), 'Testing {}'.format(heur_name)):
run = GeneticOptimization(tsp, maxeval, N=5, M=15, Tsel1=1.0, Tsel2=0.5,
mutation=mutation['mutation'],
crossover=crossover['crossover']).search()
run['run'] = i
run['heur'] = heur_name
run['mutation'] = mutation['name']
run['crossover'] = crossover['name']
runs.append(run)
res_df = pd.DataFrame(runs, columns=['heur', 'run', 'mutation', 'crossover','best_x', 'best_y', 'neval'])
results = pd.concat([results, res_df], axis=0)
# In[9]:
results_pivot = results.pivot_table(
'name': 'rnd'
},
]
# In[12]:
results = pd.DataFrame()
for crossover in crossovers:
heur_name = 'GO_{}'.format(crossover['name'])
runs = []
for i in tqdm(range(NUM_RUNS), 'Testing {}'.format(heur_name)):
run = GeneticOptimization(tsp,
maxeval,
N=5,
M=15,
Tsel1=1,
Tsel2=0.1,
mutation=CauchyMutation(
r=.75, correction=Correction(tsp)),
crossover=crossover['crossover']).search()
run['run'] = i
run['heur'] = heur_name
run['crossover'] = crossover['name']
runs.append(run)
res_df = pd.DataFrame(
runs,
columns=['heur', 'run', 'crossover', 'best_x', 'best_y', 'neval'])
results = pd.concat([results, res_df], axis=0)
# In[13]:
stats_go_3.sort_values(by='Tsel2')
stats_go_3
# The optimal GO parameters are following:
# * Size of the population: N = 2
# * Mutation probability : p = 0.10
# * Selection temperatures:
# - Tsel1 = 1e-10
# - Tsel2 = 1e-2
# In[45]:
mutation = BinaryMutation(p=0.10, correction=Correction(scp))
heur = GeneticOptimization(of=scp, maxeval=maxeval, N=2, M=6, Tsel1=1e-10, Tsel2=1e-2,
mutation=mutation, crossover=crossover)
result = heur.search()
print('neval = {}'.format(result['neval']))
print('best_x = {}'.format(result['best_x']))
print('best_y = {}'.format(result['best_y']))
# ## Fast Simulated Annealing + Genetic Optimization heursitic
# In this case, we will use FSA and GO parameters optimized separately in the previous section.
# In[46]:
from heur_go import UniformMultipoint
from heur_fsa_go import FsaGoHeuristic
from heur_aux import BinaryMutation, Correction
RandomCombination().crossover(x, y)
# They work as expected.
# Finally, let's run GO:
# In[30]:
from heur_go import GeneticOptimization
# In[31]:
heur = GeneticOptimization(dj,
maxeval=10000,
N=10,
M=30,
Tsel1=0.5,
Tsel2=0.1,
mutation=cauchy,
crossover=UniformMultipoint(1))
res = heur.search()
print(res['best_x'])
print(res['best_y'])
print(res['neval'])
# # _h_-means
#
# Heuristic cluster analysis inspired by [_k_-means](https://en.wikipedia.org/wiki/K-means_clustering). Another demonstration of how continuous heuristics can be used — example of an application.
# In[32]:
import matplotlib