Abstract:
We look at the complexity placed by big search spaces, dominated by the number of variables and domain of each variable, in search and optimization problems. While a larg...Show MoreNotes: This article was mistakenly omitted from the original submission to IEEE Xplore. It is now included as part of the conference record.
Metadata
Abstract:
We look at the complexity placed by big search spaces, dominated by the number of variables and domain of each variable, in search and optimization problems. While a large, even infinite, search domain impairs the effectiveness and efficiency of search, a complex structure of constraints further increases the difficulty in that the search space becomes highly irregular. We propose in this position paper that data mining and dimension reduction techniques have a potential in addressing the pressing issues in both combinatorial optimization and continuous optimization. By preprocessing the original search space, data mining can help boost the speed of search by guiding the search effort to a reduced, more promising area.
Notes: This article was mistakenly omitted from the original submission to IEEE Xplore. It is now included as part of the conference record.
Date of Conference: 27-30 October 2014
Date Added to IEEE Xplore: 13 July 2015
Electronic ISBN:978-1-4799-5666-1