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Learning and using hyper-heuristics for variable and value ordering in constraint satisfaction problems
2009
Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09
This paper explores the use of hyper-heuristics for variable and value ordering in binary Constraint Satisfaction Problems (CSP). Specifically, we describe the use of a symbolic cognitive architecture, augmented with constraint based reasoning as the hyper-heuristic machine learning framework. The underlying design motivation of our approach is to "do more with less." Specifically, the approach seeks to minimize the number of low level heuristics encoded yet dramatically expand the
doi:10.1145/1570256.1570304
dblp:conf/gecco/BittleF09
fatcat:yg2v2faqs5eyfauuexcik6ttym