Approximation-Guided Evolutionary Multi-Objective Optimization
Abstract
Multi-objective optimization problems arise frequently in applications but can often only be solved approximately by heuristic approaches. Evolutionary algorithms have been widely used to tackle multi-objective problems. These algorithms use different measures to ensure diversity in the objective space but are not guided by a formal notion of approximation. We present a new framework of an evolutionary algorithm for multi-objective optimization that allows to work with a formal notion of approximation. Our experimental results show that our approach outperforms state-of-the-art evolutionary algorithms in terms of the quality of the approximation that is obtained in particular for problems with many objectives.
Cite
Text
Bringmann et al. "Approximation-Guided Evolutionary Multi-Objective Optimization." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-204Markdown
[Bringmann et al. "Approximation-Guided Evolutionary Multi-Objective Optimization." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/bringmann2011ijcai-approximation/) doi:10.5591/978-1-57735-516-8/IJCAI11-204BibTeX
@inproceedings{bringmann2011ijcai-approximation,
title = {{Approximation-Guided Evolutionary Multi-Objective Optimization}},
author = {Bringmann, Karl and Friedrich, Tobias and Neumann, Frank and Wagner, Markus},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {1198-1203},
doi = {10.5591/978-1-57735-516-8/IJCAI11-204},
url = {https://mlanthology.org/ijcai/2011/bringmann2011ijcai-approximation/}
}