In this paper we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach offers two significant advantages over existing methods: it allows the evolution of more complex combinations of heuristics, and; it can identify fruitful synergies among heuristics. Using four different classes of MAX-SAT problems, we experimentally demonstrate that algorithms evolved with this method exhibit superior performance in comparison to general purpose methods. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Bain, S., Thornton, J., & Sattar, A. (2005). Evolving variable-ordering heuristics for constrained optimisation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3709 LNCS, pp. 732–736). https://doi.org/10.1007/11564751_54
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