Improving Convergence of CMA-ES Through Structure-Driven Discrete Recombination
Abstract
Evolutionary Strategies (ES) are a class of continuous optimization algorithms that have proven to perform very well on hard optimization problems. Whereas in earlier literature, both intermediate and discrete recombination operators were used, we now see that most ES, e.g. CMA-ES, use only intermediate recombination. While CMA-ES is considered state-of-the-art in continuous optimization, we believe that reintroducing discrete recombination can improve the algorithms' ability to escape local optima. Specifically, we look at using information on the problem's structure to create building blocks for recombination.
Cite
Text
Brys and Nowé. "Improving Convergence of CMA-ES Through Structure-Driven Discrete Recombination." AAAI Conference on Artificial Intelligence, 2012. doi:10.1609/AAAI.V26I1.8412Markdown
[Brys and Nowé. "Improving Convergence of CMA-ES Through Structure-Driven Discrete Recombination." AAAI Conference on Artificial Intelligence, 2012.](https://mlanthology.org/aaai/2012/brys2012aaai-improving/) doi:10.1609/AAAI.V26I1.8412BibTeX
@inproceedings{brys2012aaai-improving,
title = {{Improving Convergence of CMA-ES Through Structure-Driven Discrete Recombination}},
author = {Brys, Tim and Nowé, Ann},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2012},
pages = {2415-2416},
doi = {10.1609/AAAI.V26I1.8412},
url = {https://mlanthology.org/aaai/2012/brys2012aaai-improving/}
}