Speedup Learning for Repair-Based Search by Identifying Redundant Steps
Abstract
Repair-based search algorithms start with an initial solution and attempt to improve it by iteratively applying repair operators. Such algorithms can often handle large-scale problems that may be difficult for systematic search algorithms. Nevertheless, the computational cost of solving such problems is still very high. We observed that many of the repair steps applied by such algorithms are redundant in the sense that they do not eventually contribute to finding a solution. Such redundant steps are particularly harmful in repair-based search, where each step carries high cost due to the very high branching factor typically associated with it.
Cite
Text
Markovitch and Shatil. "Speedup Learning for Repair-Based Search by Identifying Redundant Steps." Journal of Machine Learning Research, 2003.Markdown
[Markovitch and Shatil. "Speedup Learning for Repair-Based Search by Identifying Redundant Steps." Journal of Machine Learning Research, 2003.](https://mlanthology.org/jmlr/2003/markovitch2003jmlr-speedup/)BibTeX
@article{markovitch2003jmlr-speedup,
title = {{Speedup Learning for Repair-Based Search by Identifying Redundant Steps}},
author = {Markovitch, Shaul and Shatil, Asaf},
journal = {Journal of Machine Learning Research},
year = {2003},
pages = {649-682},
volume = {4},
url = {https://mlanthology.org/jmlr/2003/markovitch2003jmlr-speedup/}
}