Constraint Satisfaction Using a Hybrid Evolutionary Hill-Climbing Algorithm That Performs Opportunistic Arc and Path Revision
Abstract
This paper introduces a hybrid evolutionary hill-climbing algorithm that quickly solves Constraint. Satisfaction Problems (CSPs). This hyhrid uses opportunistic arc and path revision in an interleaved fashion to reduce the size of the search space and to realize when to quit if a CSP is based on an inconsistent constraint network. This hybrid outperforms a well known hill-climbing algorithm, the Iterative Descent Method on a test suite of 750 randomly generated CSPs.
Cite
Text
Bowen and Dozier. "Constraint Satisfaction Using a Hybrid Evolutionary Hill-Climbing Algorithm That Performs Opportunistic Arc and Path Revision." AAAI Conference on Artificial Intelligence, 1996.Markdown
[Bowen and Dozier. "Constraint Satisfaction Using a Hybrid Evolutionary Hill-Climbing Algorithm That Performs Opportunistic Arc and Path Revision." AAAI Conference on Artificial Intelligence, 1996.](https://mlanthology.org/aaai/1996/bowen1996aaai-constraint/)BibTeX
@inproceedings{bowen1996aaai-constraint,
title = {{Constraint Satisfaction Using a Hybrid Evolutionary Hill-Climbing Algorithm That Performs Opportunistic Arc and Path Revision}},
author = {Bowen, James and Dozier, Gerry V.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1996},
pages = {326-331},
url = {https://mlanthology.org/aaai/1996/bowen1996aaai-constraint/}
}