.. is the problem of choosing a set of optimal hyperparametersÂ
 should be minimized
Multi-obj algorithm (NSGA-II)
Multi-obj algorithm (NSGA-II)
x400 samples
Basic | Compositional |
---|---|
costly | cheap target function |
static | adaptive |
state-of-the-art solution | near-optimal solution |
Classical single
optimization
Multi-/Many-obj
optimization
Compositional
Multi-objective
Obj-1
Obj-2
Samples
NSGA-II
Obj-1
Obj-2
Samples
NSGA-II
Decision tree
Good, Fast, Cheap: Pick any two (you can't have all three)
 should be minimized
class Model(ABC):
@abstractmethod
def build_model(self): pass
@abstractmethod
def validate_model(self): pass
@abstractmethod
def predict_next_configurations(self, amount):
# TODO: Make it `template method` or 'strategy'.
return Configuration
@abstractmethod
def update_data(self, configurations: List[Configuration]):
return self
ZDT-4
Energy consumption: nanozip