1. Installation
2. Usage
3. Parameters

Movado needs python 3.6 or higher. Install it through pip (on many linux systems you need to use pip3 to force python3 installation):

pip3 install --user --no-cache movado

Movado exposes just an annotation which you need to apply to the fitness function you want to approximate:

@approximate(outputs=3)
def fitness(point: List[Number]) -> List[Number]:
    # my fitness logic

class SomeClass:
    def __init__():
        ...
    @approximate(outputs=1)
    def fitness(self, point: List[Number]) -> List[Number]:
        # this is also allowed

The only mandatory parameter to approximate is the number of objectives of your optimization problem, in the above case the fitness function returns a list of 3 floats and takes as input a list of numbers. Fitness Function Structure:

1. the fitness function must take only a list of numbers as input. If we approximate a method in a class self is allowed as first parameter but it must be strictly followed by the list of numbers
2. the fitness function must output a list of numbers with constant length between fitness evaluation

Other than outputs several other optional parameters are available

@inproceedings{paletti2021online,
  title={Online Learning RTL Synthesis for Automated Design Space Exploration},
  author={Paletti, Daniele and Peverelli, Francesco and Conficconi, Davide and Santambrogio, Marco D},
  booktitle={2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
  year={2022},
  organization={IEEE}
}