CMA-HAGA-release
CMA-PAES-HAGA implementation in Python.
Change log
- v0.1 - 23-May-17 - first commit
Recommended parameters
delta = 2 mu = 100, CMA-PAES-HAGA operates on small population sizes regardless of the number of objectives being considered.
Relevant publications
@article{rostami2016covariance,
title={Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm},
author={Rostami, Shahin and Neri, Ferrante},
journal={Integrated Computer-Aided Engineering},
volume={23},
number={4},
pages={313--329},
year={2016},
publisher={IOS Press}
}
@article{rostami2016fast,
title={A fast hypervolume driven selection mechanism for many-objective optimisation problems},
author={Rostami, Shahin and Neri, Ferrante},
journal={Swarm and Evolutionary Computation},
year={2016},
publisher={Elsevier}
}
@inproceedings{rostami2012cma,
title={Cma-paes: Pareto archived evolution strategy using covariance matrix adaptation for multi-objective optimisation},
author={Rostami, Shahin and Shenfield, Alex},
booktitle={Computational Intelligence (UKCI), 2012 12th UK Workshop on},
pages={1--8},
year={2012},
organization={IEEE}
}