def get_po_toolbox(predictors, response):
creator.create("FitnessAge", base.Fitness, weights=(WEIGHT_FITNESS, WEIGHT_AGE_DENSITY))
creator.create("Individual", SemanticPrimitiveTree, fitness=creator.FitnessAge, age=int)
toolbox = base.Toolbox()
pset = symbreg.get_numpy_pset(len(predictors[0]))
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=MIN_DEPTH_INIT, max_=MAX_DEPTH_INIT)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", initialization.syntactically_distinct, individual=toolbox.individual, retries=100)
toolbox.register("select", tools.selRandom)
expression_dict = cachetools.LRUCache(maxsize=10000)
toolbox.register("error_func", ERROR_FUNCTION, response=response)
toolbox.register("evaluate_error", semantics.calc_eval_semantics, context=pset.context, predictors=predictors,
eval_semantics=toolbox.error_func, expression_dict=expression_dict)
toolbox.register("koza_node_selector", operators.internally_biased_node_selector, bias=INTERNAL_NODE_SELECTION_BIAS)
toolbox.register("mate", operators.one_point_xover_biased, node_selector=toolbox.koza_node_selector)
toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=MAX_HEIGHT))
toolbox.decorate("mate", gp.staticLimit(key=len, max_value=MAX_SIZE))
mstats = reports.configure_inf_protected_stats()
multi_archive = get_archive(response)
pop = toolbox.population(n=POP_SIZE)
toolbox.register("run", afpo.pareto_optimization, population=pop, toolbox=toolbox, xover_prob=XOVER_PROB,
mut_prob=MUT_PROB, ngen=NGEN, tournament_size=TOURN_SIZE, num_randoms=1, stats=mstats,
archive=multi_archive, calc_pareto_front=False)
toolbox.register("save", reports.save_log_to_csv)
toolbox.decorate("save", reports.save_archive(multi_archive))
return toolbox
def get_generic_toolbox(predictors, response, test_predictors, test_response, register_mut_func, mut_pb, xover_pb,
node_selector, size_limit=MAX_SIZE):
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
pset = symbreg.get_numpy_pset(len(predictors[0]))
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=MIN_DEPTH_INIT, max_=MAX_DEPTH_INIT)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", initialization.syntactically_distinct, individual=toolbox.individual, retries=100)
toolbox.register("select", tools.selTournament, tournsize=TOURNAMENT_SIZE)
expression_dict = cachetools.LRUCache(maxsize=10000)
toolbox.register("semantic_calculator", semantics.calculate_semantics, context=pset.context, predictors=predictors,
expression_dict=expression_dict)
toolbox.register("error_func", ERROR_FUNCTION, response=response)
toolbox.register("evaluate_error", semantics.calc_eval_semantics, context=pset.context, predictors=predictors,
eval_semantics=toolbox.error_func, expression_dict=expression_dict)
toolbox.register("assign_fitness", ea_simple_semantics.assign_raw_fitness)
toolbox.register("mate", operators.one_point_xover_biased, node_selector=node_selector)
toolbox.decorate("mate", operators.static_limit(key=operator.attrgetter("height"), max_value=MAX_HEIGHT))
toolbox.decorate("mate", operators.static_limit(key=len, max_value=size_limit))
lib = library.PopulationLibrary()
register_mut_func(toolbox, lib, pset, response, node_selector)
toolbox.decorate("mutate", operators.static_limit(key=operator.attrgetter("height"), max_value=MAX_HEIGHT))
toolbox.decorate("mutate", operators.static_limit(key=len, max_value=size_limit))
mstats = reports.configure_inf_protected_stats()
multi_archive = get_archive(pset, test_predictors, test_response)
multi_archive.archives.append(lib)
pop = toolbox.population(n=POP_SIZE)
toolbox.register("run", ea_simple_semantics.ea_simple, population=pop, toolbox=toolbox, cxpb=xover_pb,
mutpb=mut_pb, ngen=NGEN, elite_size=0, stats=mstats, verbose=True, archive=multi_archive)
toolbox.register("save", reports.save_log_to_csv)
toolbox.decorate("save", reports.save_archive(multi_archive))
return toolbox
def get_toolbox(predictors, response, pset, test_predictors=None, test_response=None):
creator.create("ErrorAgeSizeComplexity", base.Fitness, weights=(-1.0, -1.0, -1.0, -1.0))
creator.create("Individual", gp.PrimitiveTree, fitness=creator.ErrorAgeSizeComplexity, age=int)
toolbox = base.Toolbox()
toolbox.register("expr", sp.generate_parametrized_expression,
partial(gp.genFull, pset=pset, min_=MIN_DEPTH_INIT, max_=MAX_DEPTH_INIT), predictors)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("compile", gp.compile, pset=pset)
toolbox.register("select", tools.selRandom)
toolbox.register("koza_node_selector", operators.internally_biased_node_selector, bias=INTERNAL_NODE_SELECTION_BIAS)
toolbox.register("mate", operators.one_point_xover_biased, node_selector=toolbox.koza_node_selector)
toolbox.decorate("mate", operators.static_limit(key=operator.attrgetter("height"), max_value=MAX_HEIGHT))
toolbox.decorate("mate", operators.static_limit(key=len, max_value=MAX_SIZE))
toolbox.register("grow", sp.generate_parametrized_expression,
partial(gp.genGrow, pset=pset, min_=MIN_GEN_GROW, max_=MAX_GEN_GROW), predictors)
toolbox.register("mutate", operators.mutation_biased, expr=toolbox.grow, node_selector=toolbox.koza_node_selector)
toolbox.decorate("mutate", operators.static_limit(key=operator.attrgetter("height"), max_value=MAX_HEIGHT))
toolbox.decorate("mutate", operators.static_limit(key=len, max_value=MAX_SIZE))
expression_dict = cachetools.LRUCache(maxsize=1000)
subset_selection_archive = subset_selection.RandomSubsetSelectionArchive(frequency=SUBSET_CHANGE_FREQUENCY,
predictors=predictors, response=response,
subset_size=SUBSET_SIZE,
expression_dict=expression_dict)
toolbox.register("error_func", partial(ERROR_FUNCTION, response))
toolbox.register("evaluate_error", sp.simple_parametrized_evaluate, context=pset.context, predictors=predictors,
error_function=toolbox.error_func, expression_dict=expression_dict)
toolbox.register("assign_fitness", afpo.assign_age_fitness_size_complexity)
multi_archive = utils.get_archive()
multi_archive.archives.append(subset_selection_archive)
mstats = reports.configure_inf_protected_stats()
pop = toolbox.population(n=POP_SIZE)
toolbox.register("run", afpo.pareto_optimization, population=pop, toolbox=toolbox, xover_prob=XOVER_PROB,
mut_prob=MUT_PROB, ngen=NGEN, tournament_size=TOURNAMENT_SIZE, num_randoms=1, stats=mstats,
archive=multi_archive, calc_pareto_front=False, verbose=False, reevaluate_population=True)
toolbox.register("save", reports.save_log_to_csv)
toolbox.decorate("save", reports.save_archive(multi_archive))
return toolbox
def get_gp_lgx_toolbox(predictors, response):
creator.create("FitnessMax", base.Fitness, weights=(WEIGHT_FITNESS,))
creator.create("Individual", SemanticPrimitiveTree, fitness=creator.FitnessMax, age=int)
toolbox = base.Toolbox()
pset = symbreg.get_numpy_pset(len(predictors[0]))
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=MIN_DEPTH_INIT, max_=MAX_DEPTH_INIT)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", initialization.syntactically_distinct, individual=toolbox.individual, retries=100)
toolbox.register("select", tools.selTournament, tournsize=TOURN_SIZE)
lib = library.SemanticLibrary()
lib.generate_trees(pset, LIBRARY_DEPTH, predictors)
toolbox.register("lib_selector", lib.get_closest, distance_measure=distances.cumulative_absolute_difference,
k=LIBRARY_SEARCH_NEIGHBORS, check_constant=True)
toolbox.register("mate", locally_geometric.homologous_lgx, library_selector=toolbox.lib_selector,
internal_bias=INTERNAL_NODE_SELECTION_BIAS, max_height=MAX_HEIGHT,
distance_measure=distances.cumulative_absolute_difference, distance_threshold=0.0)
toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=MAX_HEIGHT))
toolbox.decorate("mate", gp.staticLimit(key=len, max_value=MAX_SIZE))
expression_dict = cachetools.LRUCache(maxsize=10000)
toolbox.register("error_func", ERROR_FUNCTION, response=response)
toolbox.register("evaluate_error", semantics.calc_eval_semantics, context=pset.context, predictors=predictors,
eval_semantics=toolbox.error_func, expression_dict=expression_dict)
toolbox.register("assign_fitness", afpo.assign_pure_fitness)
mstats = reports.configure_inf_protected_stats()
multi_archive = get_archive(response)
pop = toolbox.population(n=POP_SIZE)
toolbox.register("run", ea_simple_semantics.ea_simple, population=pop, toolbox=toolbox, cxpb=XOVER_PROB,
mutpb=MUT_PROB, ngen=NGEN, elite_size=0, stats=mstats, verbose=False, archive=multi_archive)
toolbox.register("save", reports.save_log_to_csv)
toolbox.decorate("save", reports.save_archive(multi_archive))
return toolbox
def get_gp_toolbox(predictors, response):
creator.create("FitnessMax", base.Fitness, weights=(WEIGHT_FITNESS, ))
creator.create("Individual",
SemanticPrimitiveTree,
fitness=creator.FitnessMax,
age=int)
toolbox = base.Toolbox()
pset = symbreg.get_numpy_pset(len(predictors[0]))
toolbox.register("expr",
gp.genHalfAndHalf,
pset=pset,
min_=MIN_DEPTH_INIT,
max_=MAX_DEPTH_INIT)
toolbox.register("individual", tools.initIterate, creator.Individual,
toolbox.expr)
toolbox.register("population",
initialization.syntactically_distinct,
individual=toolbox.individual,
retries=100)
toolbox.register("select", tools.selTournament, tournsize=TOURN_SIZE)
expression_dict = cachetools.LRUCache(maxsize=10000)
toolbox.register("error_func", ERROR_FUNCTION, response=response)
toolbox.register("evaluate_error",
semantics.calc_eval_semantics,
context=pset.context,
predictors=predictors,
eval_semantics=toolbox.error_func,
expression_dict=expression_dict)
toolbox.register("assign_fitness", afpo.assign_pure_fitness)
toolbox.register("koza_node_selector",
operators.internally_biased_node_selector,
bias=INTERNAL_NODE_SELECTION_BIAS)
toolbox.register("mate",
operators.one_point_xover_biased,
node_selector=toolbox.koza_node_selector)
toolbox.decorate(
"mate",
gp.staticLimit(key=operator.attrgetter("height"),
max_value=MAX_HEIGHT))
toolbox.decorate("mate", gp.staticLimit(key=len, max_value=MAX_SIZE))
mstats = reports.configure_inf_protected_stats()
multi_archive = get_archive(response)
pop = toolbox.population(n=POP_SIZE)
toolbox.register("run",
ea_simple_semantics.ea_simple,
population=pop,
toolbox=toolbox,
cxpb=XOVER_PROB,
mutpb=MUT_PROB,
ngen=NGEN,
elite_size=0,
stats=mstats,
verbose=False,
archive=multi_archive)
toolbox.register("save", reports.save_log_to_csv)
toolbox.decorate("save", reports.save_archive(multi_archive))
return toolbox
def get_novelty_po_lgx_toolbox(predictors, response):
creator.create("FitnessNovelty",
base.Fitness,
weights=(WEIGHT_FITNESS, WEIGHT_NOVELTY))
creator.create("Individual",
SemanticPrimitiveTree,
fitness=creator.FitnessNovelty,
age=int)
toolbox = base.Toolbox()
pset = symbreg.get_numpy_pset(len(predictors[0]))
toolbox.register("expr",
gp.genHalfAndHalf,
pset=pset,
min_=MIN_DEPTH_INIT,
max_=MAX_DEPTH_INIT)
toolbox.register("individual", tools.initIterate, creator.Individual,
toolbox.expr)
toolbox.register("population",
initialization.syntactically_distinct,
individual=toolbox.individual,
retries=100)
toolbox.register("select", tools.selRandom)
lib = library.SemanticLibrary()
lib.generate_trees(pset, LIBRARY_DEPTH, predictors)
toolbox.register("lib_selector",
lib.get_closest,
distance_measure=distances.cumulative_absolute_difference,
k=LIBRARY_SEARCH_NEIGHBORS,
check_constant=True)
toolbox.register("mate",
locally_geometric.homologous_lgx,
library_selector=toolbox.lib_selector,
internal_bias=INTERNAL_NODE_SELECTION_BIAS,
max_height=MAX_HEIGHT,
distance_measure=distances.cumulative_absolute_difference,
distance_threshold=0.0)
toolbox.decorate(
"mate",
gp.staticLimit(key=operator.attrgetter("height"),
max_value=MAX_HEIGHT))
toolbox.decorate("mate", gp.staticLimit(key=len, max_value=MAX_SIZE))
expression_dict = cachetools.LRUCache(maxsize=10000)
toolbox.register("error_func", ERROR_FUNCTION, response=response)
toolbox.register("evaluate_error",
semantics.calc_eval_semantics,
context=pset.context,
predictors=predictors,
eval_semantics=toolbox.error_func,
expression_dict=expression_dict)
mstats = reports.configure_inf_protected_stats()
multi_archive = get_archive(response)
pop = toolbox.population(n=POP_SIZE)
toolbox.register("run",
afpo.pareto_optimization,
population=pop,
toolbox=toolbox,
xover_prob=XOVER_PROB,
mut_prob=MUT_PROB,
ngen=NGEN,
tournament_size=TOURN_SIZE,
num_randoms=1,
stats=mstats,
archive=multi_archive,
calc_pareto_front=False)
toolbox.register("save", reports.save_log_to_csv)
toolbox.decorate("save", reports.save_archive(multi_archive))
return toolbox
def get_toolbox_base(predictors, response, toolbox, param_mut_prob):
metadata_dict = dict()
latitude_longitude = np.load('../data/SweData/metadata/latlon.npy')
elevation = np.load('../data/SweData/metadata/elevation.npy')
aspect = np.load('../data/SweData/metadata/aspect.npy')
metadata_dict["LatLon"] = latitude_longitude
metadata_dict["Elevation"] = np.repeat(elevation, 3)
metadata_dict["Aspect"] = np.repeat(aspect, 3)
metadata_dict["Response"] = response
predictors_dict = [None, None, None]
predictors_indices = np.arange(predictors.shape[1])
predictors_dict[0] = predictors[:, predictors_indices % 3 == 0]
predictors_dict[1] = predictors[:, predictors_indices % 3 == 1]
predictors_dict[2] = predictors[:, predictors_indices % 3 == 2]
metadata_dict["Predictors"] = predictors_dict
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("select", tools.selRandom)
# Crossover
toolbox.register("mate", gp.cxOnePoint)
toolbox.decorate(
"mate", gp.staticLimit(key=operator.attrgetter("height"),
max_value=17))
toolbox.decorate("mate", gp.staticLimit(key=len, max_value=300))
# Mutation
toolbox.register("expr_mutation", gp.genFull, min_=0, max_=2)
toolbox.register("subtree_mutate",
gp.mutUniform,
expr=toolbox.expr_mutation,
pset=toolbox.pset)
toolbox.decorate(
"subtree_mutate",
gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
toolbox.decorate("subtree_mutate", gp.staticLimit(key=len, max_value=300))
toolbox.register("parameter_mutation",
mutation.one_point_parameter_mutation,
toolbox=toolbox,
metadata=metadata_dict,
two_point_scale=0.005,
radius_scale=0.25,
iterations=20)
toolbox.register(
"mutate",
mutation.multi_mutation,
mutations=[toolbox.subtree_mutate, toolbox.parameter_mutation],
probs=[0.05, param_mut_prob])
# Fast evaluation configuration
numpy_response = np.array(response)
numpy_predictors = np.array(predictors)
expression_dict = cachetools.LRUCache(maxsize=2000)
toolbox.register("error_func",
fast_evaluate.anti_correlation,
response=numpy_response)
toolbox.register("evaluate_error",
fast_numpy_evaluate_metadata,
context=toolbox.pset.context,
predictors=numpy_predictors,
metadata=metadata_dict,
error_function=toolbox.error_func,
expression_dict=expression_dict,
arg_prefix="ARG")
toolbox.register("evaluate",
afpo.evaluate_age_fitness_size,
error_func=toolbox.evaluate_error)
random_data_points = np.random.choice(len(predictors), 1000, replace=False)
subset_predictors = numpy_predictors[random_data_points, :]
toolbox.register("calc_semantics",
calculate_semantics,
context=toolbox.pset.context,
predictors=subset_predictors,
metadata=metadata_dict)
toolbox.register("simplify_front",
simplify.simplify_all,
toolbox=toolbox,
size_threshold=0,
semantics_threshold=10e-5,
precompute_semantics=True)
pop = toolbox.population(n=1000)
mstats = reports.configure_inf_protected_stats()
pareto_archive = archive.ParetoFrontSavingArchive(
frequency=1,
criteria_chooser=archive.pick_fitness_size_from_fitness_age_size,
simplifier=toolbox.simplify_front)
toolbox.register("run",
afpo.afpo,
population=pop,
toolbox=toolbox,
xover_prob=0.75,
mut_prob=0.20,
ngen=1000,
tournament_size=2,
num_randoms=1,
stats=mstats,
mut_archive=None,
hall_of_fame=pareto_archive)
toolbox.register("save", reports.save_log_to_csv)
toolbox.decorate("save", reports.save_archive(pareto_archive))
return toolbox
def get_toolbox(predictors, response):
creator.create("ErrorAgeSize", base.Fitness, weights=(-1.0, -1.0, -1.0))
creator.create("Individual",
gp.PrimitiveTree,
fitness=creator.ErrorAgeSize)
toolbox = base.Toolbox()
pset = symbreg.get_numpy_polynomial_explog_trig_pset(len(predictors[0]))
pset.addEphemeralConstant("gaussian", lambda: random.gauss(0.0, 1.0))
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=6)
toolbox.register("individual", tools.initIterate, creator.Individual,
toolbox.expr)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("select", tools.selRandom)
# Crossover
toolbox.register("mate", gp.cxOnePoint)
toolbox.decorate(
"mate", gp.staticLimit(key=operator.attrgetter("height"),
max_value=17))
toolbox.decorate("mate", gp.staticLimit(key=len, max_value=300))
# Mutation
toolbox.register("expr_mutation", gp.genFull, min_=0, max_=2)
toolbox.register("mutate",
gp.mutUniform,
expr=toolbox.expr_mutation,
pset=pset)
toolbox.decorate(
"mutate",
gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
toolbox.decorate("mutate", gp.staticLimit(key=len, max_value=300))
# Fast evaluation configuration
numpy_response = numpy.array(response)
numpy_predictors = numpy.array(predictors)
expression_dict = cachetools.LRUCache(maxsize=2000)
# toolbox.register("error_func", fast_evaluate.mean_absolute_percentage_error, response=numpy_response)
toolbox.register("error_func",
fast_evaluate.anti_correlation,
response=numpy_response)
toolbox.register("evaluate_error",
fast_evaluate.fast_numpy_evaluate,
context=pset.context,
predictors=numpy_predictors,
error_function=toolbox.error_func,
expression_dict=expression_dict)
toolbox.register("evaluate",
afpo.evaluate_age_fitness_size,
error_func=toolbox.evaluate_error)
random_data_points = numpy.random.choice(len(predictors),
1000,
replace=False)
subset_predictors = numpy_predictors[random_data_points, :]
toolbox.register("calc_semantics",
semantics.calculate_semantics,
context=pset.context,
predictors=subset_predictors)
toolbox.register("simplify_front",
simplify.simplify_all,
toolbox=toolbox,
size_threshold=0,
semantics_threshold=10e-5,
precompute_semantics=True)
pop = toolbox.population(n=1000)
mstats = reports.configure_inf_protected_stats()
pareto_archive = archive.ParetoFrontSavingArchive(
frequency=1,
criteria_chooser=archive.pick_fitness_size_from_fitness_age_size,
simplifier=toolbox.simplify_front)
toolbox.register("run",
afpo.afpo,
population=pop,
toolbox=toolbox,
xover_prob=0.75,
mut_prob=0.01,
ngen=1000,
tournament_size=2,
num_randoms=1,
stats=mstats,
hall_of_fame=pareto_archive)
toolbox.register("save", reports.save_log_to_csv)
toolbox.decorate("save", reports.save_archive(pareto_archive))
return toolbox
def get_toolbox_base(predictors, response, toolbox, param_mut_prob):
metadata_dict = dict()
latitude_longitude = np.load('../data/SweData/metadata/latlon.npy')
elevation = np.load('../data/SweData/metadata/elevation.npy')
aspect = np.load('../data/SweData/metadata/aspect.npy')
metadata_dict["LatLon"] = latitude_longitude
metadata_dict["Elevation"] = np.repeat(elevation, 3)
metadata_dict["Aspect"] = np.repeat(aspect, 3)
metadata_dict["Response"] = response
predictors_dict = [None, None, None]
predictors_indices = np.arange(predictors.shape[1])
predictors_dict[0] = predictors[:, predictors_indices % 3 == 0]
predictors_dict[1] = predictors[:, predictors_indices % 3 == 1]
predictors_dict[2] = predictors[:, predictors_indices % 3 == 2]
metadata_dict["Predictors"] = predictors_dict
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("select", tools.selRandom)
# Crossover
toolbox.register("mate", gp.cxOnePoint)
toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
toolbox.decorate("mate", gp.staticLimit(key=len, max_value=300))
# Mutation
toolbox.register("expr_mutation", gp.genFull, min_=0, max_=2)
toolbox.register("subtree_mutate", gp.mutUniform, expr=toolbox.expr_mutation, pset=toolbox.pset)
toolbox.decorate("subtree_mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
toolbox.decorate("subtree_mutate", gp.staticLimit(key=len, max_value=300))
toolbox.register("parameter_mutation", mutation.one_point_parameter_mutation,
toolbox=toolbox, metadata=metadata_dict, two_point_scale=0.005, radius_scale=0.25, iterations=20)
toolbox.register("mutate", mutation.multi_mutation,
mutations=[toolbox.subtree_mutate, toolbox.parameter_mutation], probs=[0.05, param_mut_prob])
# Fast evaluation configuration
numpy_response = np.array(response)
numpy_predictors = np.array(predictors)
expression_dict = cachetools.LRUCache(maxsize=2000)
toolbox.register("error_func", fast_evaluate.anti_correlation, response=numpy_response)
toolbox.register("evaluate_error", fast_numpy_evaluate_metadata, context=toolbox.pset.context,
predictors=numpy_predictors, metadata=metadata_dict, error_function=toolbox.error_func,
expression_dict=expression_dict, arg_prefix="ARG")
toolbox.register("evaluate", afpo.evaluate_age_fitness_size, error_func=toolbox.evaluate_error)
random_data_points = np.random.choice(len(predictors), 1000, replace=False)
subset_predictors = numpy_predictors[random_data_points, :]
toolbox.register("calc_semantics", calculate_semantics, context=toolbox.pset.context,
predictors=subset_predictors, metadata=metadata_dict)
toolbox.register("simplify_front", simplify.simplify_all, toolbox=toolbox, size_threshold=0,
semantics_threshold=10e-5, precompute_semantics=True)
pop = toolbox.population(n=1000)
mstats = reports.configure_inf_protected_stats()
pareto_archive = archive.ParetoFrontSavingArchive(frequency=1,
criteria_chooser=archive.pick_fitness_size_from_fitness_age_size,
simplifier=toolbox.simplify_front)
toolbox.register("run", afpo.afpo, population=pop, toolbox=toolbox, xover_prob=0.75, mut_prob=0.20, ngen=1000,
tournament_size=2, num_randoms=1, stats=mstats,
mut_archive=None, hall_of_fame=pareto_archive)
toolbox.register("save", reports.save_log_to_csv)
toolbox.decorate("save", reports.save_archive(pareto_archive))
return toolbox