"Initial TV: %f" %
global_fitness_function.global_regularisation_term_set[-1])
# Counters
i = 0
stagnation = 0
number_of_mitosis = 0
g_generation = 0
# Run the evolutionary loop
run_evolutionary_loop = True
# Log the statistics
last_proportion_of_good_flies = 0
g_log_event = "Random initial population"
logStatistics(optimiser.getNumberOfIndividuals())
g_generation += 1
if args.selection[0] == "threshold":
selection_operator.max_iteration_reached_counter = -100
addToFitnessCircularList()
previous_gradient_global_fitness = None
previous_gradient_TV = None
previous_proportion_of_good_flies = 0
while run_evolutionary_loop:
# The max number of generations has not been reached
if i < number_of_generation:
logging.debug("Initial RMSE: %f" % global_fitness_function.global_error_term_set[-1]);
logging.debug("Initial TV: %f" % global_fitness_function.global_regularisation_term_set[-1]);
# Counters
i = 0;
stagnation = 0;
number_of_mitosis = 0;
g_iteration = 0;
# Run the optimisation loop
run_optimisation_loop = True;
next_is_slautering = False;
# Log the statistics
g_log_event="Random initial population"; logStatistics(optimiser.getNumberOfIndividuals()); g_iteration += 1;
global_fitness_before_mitosis = None;
g_generation = 0;
while run_optimisation_loop:
# The max number of generations has not been reached
if i < number_of_iterations:
# Stagnation has been reached
stagnation_reached = False;
if stagnation >= args.max_stagnation_counter[0] and args.max_stagnation_counter[0] > 0:
stagnation_reached = True;
logging.debug(
"Initial TV: %f" %
global_fitness_function.global_regularisation_term_set[-1])
# Counters
i = 0
stagnation = 0
number_of_mitosis = 0
g_generation = 0
# Run the evolutionary loop
run_evolutionary_loop = True
# Log the statistics
g_log_event = "Random initial population"
logStatistics(optimiser.getNumberOfIndividuals())
g_generation += 1
while run_evolutionary_loop:
# The max number of generations has not been reached
if i < number_of_generation:
# Stagnation has been reached
if stagnation >= args.max_stagnation_counter[0]:
# Exit the for loop
run_evolutionary_loop = False
# Log message
if not isinstance(args.logging, NoneType):
if not isinstance(args.logging, NoneType):
logging.debug("Initial Global fitness: %f" % best_global_fitness);
logging.debug("Initial RMSE: %f" % global_fitness_function.global_error_term_set[-1]);
logging.debug("Initial TV: %f" % global_fitness_function.global_regularisation_term_set[-1]);
# Counters
i = 0;
stagnation = 0;
number_of_mitosis = 0;
g_iteration = 0;
# Run the optimisation loop
run_optimisation_loop = True;
# Log the statistics
g_log_event="Random initial population"; logStatistics(optimiser.getNumberOfIndividuals()); g_iteration += 1;
print(i, optimiser.best_solution.objective);
while run_optimisation_loop:
# The max number of generations has not been reached
if i < number_of_iterations:
# Stagnation has been reached
if stagnation >= args.max_stagnation_counter[0] and args.max_stagnation_counter[0] > 0:
# Exit the for loop
run_optimisation_loop = False;
# Log message