selection_operator = ThresholdSelection(0, tournament_selection, 10)
else:
raise ValueError(
'Invalid selection operator "%s". Choose "threshold", "tournament" or "dual".'
% (args.selection[0]))
optimiser.setSelectionOperator(selection_operator)
# Create the genetic operators
new_blood = NewBloodOperator(args.initial_new_blood_probability[0])
gaussian_mutation = GaussianMutationOperator(
1.0 - args.initial_new_blood_probability[0],
args.initial_mutation_variance[0])
# Add the genetic operators to the EA
optimiser.addGeneticOperator(new_blood)
optimiser.addGeneticOperator(gaussian_mutation)
# Show the visualisation
if args.visualisation:
fig, ax = plt.subplots(7, 2)
global_fitness_function.plot(fig, ax, 0, number_of_generation)
# Create a progress bar
bar = MyBar('Generation', max=number_of_generation)
best_global_fitness = global_fitness_function.global_fitness_set[-1]
# Log message
if not isinstance(args.logging, NoneType):
logging.debug("Initial Global fitness: %f" % best_global_fitness)
logging.debug("Initial RMSE: %f" %
# 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) EA_number_of_evaluation = test_problem.number_of_evaluation EA_solution = optimiser.best_solution # Optimisation and visualisation test_problem.number_of_evaluation = 0 optimiser = PSO(test_problem, g_number_of_individuals) optimiser.plotAnimation(g_iterations) PSO_number_of_evaluation = test_problem.number_of_evaluation PSO_solution = optimiser.best_solution
elif args.selection[0] == "ranking":
optimiser.setSelectionOperator(RankSelection())
elif args.selection[0] == "roulette":
optimiser.setSelectionOperator(RouletteWheelSelection())
else:
raise ValueError(
'Invalid selection operator "%s". Choose "threshold", "tournament" or "dual".'
% (args.selection[0]))
# Create the genetic operators
gaussian_mutation = GaussianMutationOperator(
0.3, args.initial_mutation_variance[0])
blend_cross_over = BlendCrossoverOperator(0.6, gaussian_mutation)
# Add the genetic operators to the EA
optimiser.addGeneticOperator(blend_cross_over)
optimiser.addGeneticOperator(gaussian_mutation)
optimiser.addGeneticOperator(ElitismOperator(0.1))
# Show the visualisation
if args.visualisation:
fig, ax = plt.subplots(7, 2)
global_fitness_function.plot(fig, ax, 0, number_of_generation)
# Create a progress bar
bar = MyBar('Generation', max=number_of_generation)
best_global_fitness = global_fitness_function.global_fitness_set[-1]
# Log message
if not isinstance(args.logging, NoneType):
logging.debug("Initial Global fitness: %f" % best_global_fitness)