# Update the mutation variance so that it varies linearly from g_max_mutation_sigma to
# g_min_mutation_sigma
g_current_sigma -= (g_max_mutation_sigma -
g_min_mutation_sigma) / (g_iterations - 1)
# Make sure the mutation variance is up-to-date
gaussian_mutation.setMutationVariance(g_current_sigma)
# Optimisation and visualisation
optimiser = EvolutionaryAlgorithm(test_problem, g_number_of_individuals)
# Set the selection operator
#optimiser.setSelectionOperator(TournamentSelection(2));
#optimiser.setSelectionOperator(RouletteWheel());
optimiser.setSelectionOperator(RankSelection())
# Create the genetic operators
elitism = ElitismOperator(0.1)
new_blood = NewBloodOperator(0.1)
gaussian_mutation = GaussianMutationOperator(0.1, 0.2)
blend_cross_over = BlendCrossoverOperator(0.6, gaussian_mutation)
# Add the genetic operators to the EA
optimiser.addGeneticOperator(new_blood)
optimiser.addGeneticOperator(gaussian_mutation)
optimiser.addGeneticOperator(blend_cross_over)
optimiser.addGeneticOperator(elitism)
test_problem.number_of_evaluation = 0
optimiser.plotAnimation(g_iterations, visualisationCallback)
def run(test_problem,
max_iterations: int,
number_of_runs: int,
file_prefix: str,
tol=-1,
visualisation=False,
aPreCallback=None,
aPostCallback=None):
global g_test_problem
global g_iterations
g_test_problem = test_problem
# Store the results for each optimisation method
columns = ['Run', 'Methods']
for i in range(test_problem.number_of_dimensions):
columns.append("X_" + str(i))
columns.append("Objective value")
columns.append("Euclidean distance")
columns.append("Evaluations")
df = pd.DataFrame(columns=columns)
for run_id in range(number_of_runs):
print("Run #", run_id)
# Create a random guess common to all the optimisation methods
initial_guess = g_test_problem.initialRandomGuess()
# Optimisation methods implemented in scipy.optimize
methods = [
'Nelder-Mead', 'Powell', 'CG', 'BFGS', 'L-BFGS-B', 'TNC', 'COBYLA',
'SLSQP'
]
for method in methods:
g_test_problem.number_of_evaluation = 0
optimiser = ScipyMinimize(g_test_problem,
method,
tol=tol,
initial_guess=initial_guess)
print("\tOptimiser:", optimiser.full_name)
if not isinstance(aPreCallback, (str, type(None))):
aPreCallback(optimiser, file_prefix, run_id)
optimiser.setMaxIterations(max_iterations)
if run_id == 0 and visualisation:
optimiser.plotAnimation(
aNumberOfIterations=max_iterations,
aCallback=None,
aFileName=(file_prefix + "_" + optimiser.short_name +
"_%d.png"))
else:
optimiser.run()
df = appendResultToDataFrame(run_id, optimiser, df, columns,
file_prefix)
if not isinstance(aPostCallback, (str, type(None))):
aPostCallback(optimiser, file_prefix, run_id)
# Parameters for EA
g_iterations = int(max_iterations / g_number_of_individuals)
# Optimisation and visualisation
g_test_problem.number_of_evaluation = 0
optimiser = EvolutionaryAlgorithm(g_test_problem,
g_number_of_individuals,
initial_guess=initial_guess)
print("\tOptimiser:", optimiser.full_name)
if not isinstance(aPreCallback, (str, type(None))):
aPreCallback(optimiser, file_prefix, run_id)
# Set the selection operator
#optimiser.setSelectionOperator(TournamentSelection(3));
#optimiser.setSelectionOperator(RouletteWheel());
optimiser.setSelectionOperator(RankSelection())
# Create the genetic operators
gaussian_mutation = GaussianMutationOperator(0.1, 0.3)
elitism = ElitismOperator(0.1)
new_blood = NewBloodOperator(0.0)
blend_cross_over = BlendCrossoverOperator(0.6, gaussian_mutation)
# Add the genetic operators to the EA
optimiser.addGeneticOperator(new_blood)
optimiser.addGeneticOperator(gaussian_mutation)
optimiser.addGeneticOperator(blend_cross_over)
optimiser.addGeneticOperator(elitism)
if run_id == 0 and visualisation:
optimiser.plotAnimation(
aNumberOfIterations=g_iterations,
aCallback=visualisationCallback,
aFileName=(file_prefix + "_" + optimiser.short_name +
"_%d.png"))
else:
for _ in range(1, g_iterations):
optimiser.runIteration()
visualisationCallback()
df = appendResultToDataFrame(run_id, optimiser, df, columns,
file_prefix)
if not isinstance(aPostCallback, (str, type(None))):
aPostCallback(optimiser, file_prefix, run_id)
# Parameters for PSO
# Optimisation and visualisation
g_test_problem.number_of_evaluation = 0
optimiser = PSO(g_test_problem,
g_number_of_individuals,
initial_guess=initial_guess)
print("\tOptimiser:", optimiser.full_name)
if not isinstance(aPreCallback, (str, type(None))):
aPreCallback(optimiser, file_prefix, run_id)
if run_id == 0 and visualisation:
optimiser.plotAnimation(
aNumberOfIterations=g_iterations,
aCallback=visualisationCallback,
aFileName=(file_prefix + "_" + optimiser.short_name +
"_%d.png"))
else:
for _ in range(1, g_iterations):
optimiser.runIteration()
visualisationCallback()
df = appendResultToDataFrame(run_id, optimiser, df, columns,
file_prefix)
if not isinstance(aPostCallback, (str, type(None))):
aPostCallback(optimiser, file_prefix, run_id)
# Optimisation and visualisation
optimiser = PureRandomSearch(g_test_problem,
max_iterations,
initial_guess=initial_guess)
print("\tOptimiser:", optimiser.full_name)
if not isinstance(aPreCallback, (str, type(None))):
aPreCallback(optimiser, file_prefix, run_id)
g_test_problem.number_of_evaluation = 0
if run_id == 0 and visualisation:
optimiser.plotAnimation(
aNumberOfIterations=max_iterations,
aCallback=None,
aFileName=(file_prefix + "_" + optimiser.short_name +
"_%d.png"))
else:
for _ in range(max_iterations):
optimiser.runIteration()
df = appendResultToDataFrame(run_id, optimiser, df, columns,
file_prefix)
if not isinstance(aPostCallback, (str, type(None))):
aPostCallback(optimiser, file_prefix, run_id)
# Optimisation and visualisation
g_test_problem.number_of_evaluation = 0
optimiser = SimulatedAnnealing(g_test_problem,
5000,
0.04,
initial_guess=initial_guess)
print("\tOptimiser:", optimiser.full_name)
optimiser.cooling_schedule = cooling
if not isinstance(aPreCallback, (str, type(None))):
aPreCallback(optimiser, file_prefix, run_id)
if run_id == 0 and visualisation:
optimiser.plotAnimation(
aNumberOfIterations=max_iterations,
aCallback=None,
aFileName=(file_prefix + "_" + optimiser.short_name +
"_%d.png"))
else:
for _ in range(1, max_iterations):
optimiser.runIteration()
#print(optimiser.current_temperature)
df = appendResultToDataFrame(run_id, optimiser, df, columns,
file_prefix)
if not isinstance(aPostCallback, (str, type(None))):
aPostCallback(optimiser, file_prefix, run_id)
title_prefix = ""
if g_test_problem.name != "":
if g_test_problem.flag == 1:
title_prefix = "Minimisation of " + g_test_problem.name + "\n"
else:
title_prefix = "Maximisation of " + g_test_problem.name + "\n"
boxplot(df, 'Evaluations', title_prefix + 'Number of evaluations',
file_prefix + 'evaluations.pdf', False)
boxplot(
df, 'Euclidean distance',
title_prefix + 'Euclidean distance between\nsolution and ground truth',
file_prefix + 'distance.pdf', False)
plt.show()
if args.selection[0] == "dual":
tournament_size = 2;
# Update the tournament size if needed
elif not isinstance(args.tournament_size, NoneType):
if isinstance(args.tournament_size, int):
tournament_size = args.tournament_size;
else:
tournament_size = args.tournament_size[0];
# Set the selection operator
selection_operator = None;
if args.selection[0] == "dual" or args.selection[0] == "tournament":
selection_operator = TournamentSelection(tournament_size);
elif args.selection[0] == "ranking":
selection_operator = RankSelection();
elif args.selection[0] == "roulette":
selection_operator = RouletteWheelSelection();
else:
raise ValueError('Invalid selection operator "%s". Choose "threshold", "tournament" or "dual".' % (args.selection[0]))
optimiser.setSelectionOperator(selection_operator);
# Create the genetic operators
gaussian_mutation = GaussianMutationOperator(0.8, args.initial_mutation_variance[0]);
blend_cross_over = BlendCrossoverOperator(0.2, gaussian_mutation);
# Add the genetic operators to the EA
optimiser.addGeneticOperator(blend_cross_over);
optimiser.addGeneticOperator(gaussian_mutation);
optimiser.addGeneticOperator(ElitismOperator(0.1));