def update_from_args(self, args):
if args.crossover is not None:
self.crossover_method = Crossover[args.crossover + '_CROSSOVER']
if args.mutation is not None:
self.mutation_method = Mutation[args.mutation + '_MUTATION']
self.cost_budget = set_default(args.cost_budget, self.cost_budget)
self.num_iterations = set_default(args.num_iterations,
self.num_iterations)
self.max_stagnation = set_default(args.max_stagnation,
self.max_stagnation)
self.population_size = set_default(args.population,
self.population_size)
self.mutation_chance = set_default(args.mutation_chance,
self.mutation_chance)
def update_from_args(self, args):
self.max_velocity = set_default(args.max_velocity, self.max_velocity)
self.cost_budget = set_default(args.cost_budget, self.cost_budget)
self.num_iterations = set_default(args.num_iterations,
self.num_iterations)
self.max_stagnation = set_default(args.max_stagnation,
self.max_stagnation)
self.num_particles = set_default(args.population, self.num_particles)
self.inertia_parameter = set_default(args.inertia,
self.inertia_parameter)
self.local_influence_parameter = set_default(
args.local_influence, self.local_influence_parameter)
self.global_influence_parameter = set_default(
args.global_influence, self.global_influence_parameter)
if args.evaluator is not None:
self.evaluator = Evaluator[args.evaluator + '_EVALUATOR']
def update_from_args(self, args):
self.max_velocity = set_default(args.max_velocity, self.max_velocity)
self.cost_budget = set_default(args.cost_budget, self.cost_budget)
self.num_iterations = set_default(args.num_iterations,
self.num_iterations)
self.max_stagnation = set_default(args.max_stagnation,
self.max_stagnation)
self.population_size = set_default(args.population,
self.population_size)
self.mutation_chance = set_default(args.mutation_chance,
self.mutation_chance)
self.crossover_rate = set_default(args.crossover_rate,
self.crossover_rate)
if args.evaluator is not None:
self.evaluator = Evaluator[args.evaluator + '_EVALUATOR']
def update_from_args(self, args):
if args.copying is not None:
self.copying_method = Copying[args.copying + '_COPYING']
if args.selection is not None:
self.selection_method = Selection[args.selection + '_SELECTION']
if args.crossover is not None:
self.crossover_method = Crossover[args.crossover + '_CROSSOVER']
if args.mutation is not None:
self.mutation_method = Mutation[args.mutation + '_MUTATION']
self.cost_budget = set_default(args.cost_budget, self.cost_budget)
self.num_iterations = set_default(args.num_iterations, self.num_iterations)
self.max_stagnation = set_default(args.max_stagnation, self.max_stagnation)
self.population_size = set_default(args.population, self.population_size)
self.top_selection_ratio = set_default(args.top, self.top_selection_ratio)
self.bottom_selection_ratio = set_default(args.bottom, self.bottom_selection_ratio)
self.mutation_chance = set_default(args.mutation_chance, self.mutation_chance)
def update_from_args(self, args):
self.max_position = set_default(args.max_velocity, self.max_position)
self.cost_budget = set_default(args.cost_budget, self.cost_budget)
self.num_iterations = set_default(args.num_iterations,
self.num_iterations)
self.max_stagnation = set_default(args.max_stagnation,
self.max_stagnation)
self.population_size = set_default(args.population,
self.population_size)
self.follow_distance_parameter = set_default(
args.distance_influence, self.follow_distance_parameter)
self.follow_survival_parameter = set_default(
args.survival_influence, self.follow_survival_parameter)
self.min_steps = set_default(args.min_steps, self.min_steps)
self.max_steps = set_default(args.max_steps, self.max_steps)
self.steps_distance_parameter = set_default(
args.steps_distance, self.steps_distance_parameter)
self.local_search_tries = set_default(args.local_search,
self.local_search_tries)
self.follow_chance = set_default(args.follow_chance,
self.follow_chance)
if args.evaluator is not None:
self.evaluator = Evaluator[args.evaluator + '_EVALUATOR']
def update_from_args(self, args):
self.cost_budget = set_default(args.cost_budget, self.cost_budget)
self.num_iterations = set_default(args.num_iterations,
self.num_iterations)
self.max_stagnation = set_default(args.max_stagnation,
self.max_stagnation)
self.population_size = set_default(args.population,
self.population_size)
self.follow_distance_parameter = set_default(
args.distance_influence, self.follow_distance_parameter)
self.follow_survival_parameter = set_default(
args.survival_influence, self.follow_survival_parameter)
self.min_steps = set_default(args.min_steps, self.min_steps)
self.max_steps = set_default(args.max_steps, self.max_steps)
self.steps_distance_parameter = set_default(
args.steps_distance, self.steps_distance_parameter)
self.local_search_tries = set_default(args.local_search,
self.local_search_tries)
self.follow_chance = set_default(args.follow_chance,
self.follow_chance)