We propose Coevolutionary Gradient Search, a blueprint for a family of iterative learning algorithms that combine elements of local search and population-based search. The approach is applied to learning Othello strategies represented as n-tuple networks, using different search operators and modes of learning. We focus on the interplay between the continuous, directed, gradient-based search in the space of weights, and fitness-driven, combinatorial, coevolutionary search in the space of entire n-tuple networks. In an extensive experiment, we assess both the objective and relative performance of algorithms, concluding that the hybridization of search techniques improves the convergence. The best algorithms not only learn faster than constituent methods alone, but also produce top ranked strategies in the online Othello League. KrawiecS11Bib
@INPROCEEDINGS { DBLP:conf/gecco/KrawiecS11,
ABSTRACT = { We propose Coevolutionary Gradient Search, a blueprint for a family of iterative learning algorithms that combine elements of local search and population-based search. The approach is applied to learning Othello strategies represented as n-tuple networks, using different search operators and modes of learning. We focus on the interplay between the continuous, directed, gradient-based search in the space of weights, and fitness-driven, combinatorial, coevolutionary search in the space of entire n-tuple networks. In an extensive experiment, we assess both the objective and relative performance of algorithms, concluding that the hybridization of search techniques improves the convergence. The best algorithms not only learn faster than constituent methods alone, but also produce top ranked strategies in the online Othello League. },
ACMID = { 2001626 },
ADDRESS = { New York, NY, USA },
AUTHOR = { Krzysztof Krawiec and Marcin Grzegorz Szubert },
BIBSOURCE = { DBLP, http://dblp.uni-trier.de },
BOOKTITLE = { Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation },
CROSSREF = { DBLP:conf/gecco/2011 },
DOI = { 10.1145/2001576.2001626 },
EE = { http://doi.acm.org/10.1145/2001576.2001626 },
ISBN = { 978-1-4503-0557-0 },
KEYWORDS = { coevolution, n-tuple networks, othello, temporal difference learning },
LOCATION = { Dublin, Ireland },
NUMPAGES = { 8 },
PAGES = { 355--362 },
PUBLISHER = { ACM },
SERIES = { GECCO '11 },
TITLE = { Learning n-tuple networks for othello by coevolutionary gradient search },
URL = { http://doi.acm.org/10.1145/2001576.2001626 },
YEAR = { 2011 },
1 = { http://doi.acm.org/10.1145/2001576.2001626 },
2 = { https://doi.org/10.1145/2001576.2001626 },
}