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 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
#
# * correction options are: vanilla correction Correction(of), MirrorCorrection(of) and ExtensionCorrection(of),
#
# * mutations options are: CauchyMutation($\gamma$) and LevyMutation($\gamma$), where parameter $\gamma > 0$ is set by user.
#
# In the current settings there are 5 crossover, 3 correction and 2 mutation options.
# In[7]:
multipoints = [1, 2, 3]
crossovers = [
{'crossover': Crossover(), 'name': 'mix'},
{'crossover': RandomCombination(), 'name': 'rnd'}]
for multipoint in multipoints:
crossover = {'crossover': UniformMultipoint(multipoint), 'name': 'uni{}'.format(multipoint)}
crossovers.append(crossover)
corrections = [
{'correction': Correction(tsp), 'name': 'vanilla'},
{'correction': MirrorCorrection(tsp), 'name': 'mirror'},
{'correction': ExtensionCorrection(tsp), 'name': 'extension'}]
parameters = [1, 3, 5]
mutations = []
for parameter in parameters:
for correction in corrections:
mutation = {'mutation': CauchyMutation(r=parameter, correction = correction['correction']), 'name': 'cauchy{}_'
.format(parameter)+correction['name']}
mutations.append(mutation)
mutation = {'mutation': LevyMutation(scale=parameter, correction = correction['correction']), 'name': 'levy{}_'
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'])
# In[16]:
results = pd.DataFrame()
mutation = CauchyMutation(r=0.75, correction=Correction(tsp))
crossover = UniformMultipoint(1)
for N in [1, 2, 3, 5, 10, 20, 30, 100]:
res = experiment_go(of=tsp, maxeval=maxeval, num_runs=NUM_RUNS, N=N, M=N*3, Tsel1=0.1, Tsel2=1,
mutation=mutation, crossover=crossover)
results = pd.concat([results, res], axis=0)
# In[17]:
# (well-known performance criteria from previous classes)
def rel(x):
return len([n for n in x if n < np.inf])/len(x)
def mne(x):
return np.mean([n for n in x if n < np.inf])
def feo(x):
y # ## 1. Random mix (baseline class) # In[6]: from heur_go import Crossover co_rnd = Crossover() co_rnd.crossover(x, y) # ## 2. Uniform n-point crossover # In[7]: from heur_go import UniformMultipoint co_uni = UniformMultipoint(4) co_uni.crossover(x, y) # ## 3. Random combination # In[8]: from heur_go import RandomCombination co_comb = RandomCombination() co_comb.crossover(x, y) # # Demonstration # In[9]: from heur_go import GeneticOptimization
from heur_go import Crossover, UniformMultipoint, RandomCombination # In[26]: x = dj.generate_point() y = dj.generate_point() print(x) print(y) # In[27]: Crossover().crossover(x, y) # In[28]: UniformMultipoint(1).crossover(x, y) # In[29]: RandomCombination().crossover(x, y) # They work as expected. # Finally, let's run GO: # In[30]: from heur_go import GeneticOptimization # In[31]:
# ## 1. Random mix (baseline class) # In[6]: from heur_go import Crossover co_rnd = Crossover() co_rnd.crossover(x, y) # ## 2. Uniform n-point crossover # In[7]: from heur_go import UniformMultipoint co_uni = UniformMultipoint(4) co_uni.crossover(x, y) # ## 3. Random combination # In[8]: from heur_go import RandomCombination co_comb = RandomCombination() co_comb.crossover(x, y) # # Demonstration # In[9]: