Co-optimization test-based problems is a class of tasks approached typically with coevolutionary algorithms. It was recently shown that such problems exhibit underlying objectives that form internal problem structure, which can be extracted and analyzed in order to drive the search or design better algorithms. The number of underlying objectives is the dimension of the problem, which is of great importance, since it may be a predictor of problem's directlyculty. In this paper, we estimate the number of dimensions for Tic Tac Toe and the Density Classification Task. JaskowskiK11Bib
@INPROCEEDINGS { DBLP:conf/gecco/JaskowskiK11,
ABSTRACT = { Co-optimization test-based problems is a class of tasks approached typically with coevolutionary algorithms. It was recently shown that such problems exhibit underlying objectives that form internal problem structure, which can be extracted and analyzed in order to drive the search or design better algorithms. The number of underlying objectives is the dimension of the problem, which is of great importance, since it may be a predictor of problem's directlyculty. In this paper, we estimate the number of dimensions for Tic Tac Toe and the Density Classification Task. },
ADDRESS = { New York, NY, USA },
AUTHOR = { Wojciech Ja{\'s}kowski and Krzysztof Krawiec },
BIBSOURCE = { DBLP, http://dblp.uni-trier.de },
BOOKTITLE = { Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation },
CROSSREF = { DBLP:conf/gecco/2011c },
EE = { http://doi.acm.org/10.1145/2001858.2002110 },
ISBN = { 978-1-4503-0690-4 },
LOCATION = { Dublin, Ireland },
PAGES = { 829-830 },
SERIES = { GECCO '11 },
TITLE = { How many dimensions in co-optimization },
URL = { http://doi.acm.org/10.1145/2001858.2002110 },
YEAR = { 2011 },
1 = { http://doi.acm.org/10.1145/2001858.2002110 },
}