Finding Diverse Solutions of High Quality to Constraint Optimization Problems
Abstract
A number of effective techniques for constraint-based optimization can be used to generate either diverse or high-quality solutions independently, but no framework is devoted to accomplish both simultaneously. In this paper, we tackle this issue with a generic paradigm that can be implemented in most existing solvers. We show that our technique can be specialized to produce diverse solutions of high quality in the context of over-constrained problems. Furthermore, our paradigm allows us to consider diversity from a different point of view, based on generic concepts expressed by global constraints.
Cite
Text
Petit and Trapp. "Finding Diverse Solutions of High Quality to Constraint Optimization Problems." International Joint Conference on Artificial Intelligence, 2015.Markdown
[Petit and Trapp. "Finding Diverse Solutions of High Quality to Constraint Optimization Problems." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/petit2015ijcai-finding/)BibTeX
@inproceedings{petit2015ijcai-finding,
title = {{Finding Diverse Solutions of High Quality to Constraint Optimization Problems}},
author = {Petit, Thierry and Trapp, Andrew C.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2015},
pages = {260-267},
url = {https://mlanthology.org/ijcai/2015/petit2015ijcai-finding/}
}