Optimizing Selective Search in Chess
Artificial Intelligence
2010-09-06 v1 Neural and Evolutionary Computing
Abstract
In this paper we introduce a novel method for automatically tuning the search parameters of a chess program using genetic algorithms. Our results show that a large set of parameter values can be learned automatically, such that the resulting performance is comparable with that of manually tuned parameters of top tournament-playing chess programs.
Keywords
Cite
@article{arxiv.1009.0550,
title = {Optimizing Selective Search in Chess},
author = {Omid David-Tabibi and Moshe Koppel and Nathan S. Netanyahu},
journal= {arXiv preprint arXiv:1009.0550},
year = {2010}
}