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:
- the fitness function must take only a list of numbers as input. If we approximate a method in a class
selfis allowed as first parameter but it must be strictly followed by the list of numbers - the fitness function must output a list of numbers with constant length between fitness evaluation
Other than outputs several other optional parameters are available
| Parameter | Description | Default Value |
|---|---|---|
| disabled | if True the approximation | False |
| is disabled | ||
| stochastic | if True caching of fitness | False |
| calls is disabled | ||
| estimator | estimator to use | HoeffdingAdaptiveTree |
| controller | controller to user | Mab |
@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}
}