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Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem
2018
2018 7th Brazilian Conference on Intelligent Systems (BRACIS)
Solving even moderately-sized Maximum Satisfiability (Max-SAT) problems exactly can be unfeasible due to their NP-Hardness. This leads to the use of metaheuristics that find a solution without exactness guarantees but run in a reasonable time. Yet, choosing the best metaheuristic to solve a MaxSAT problem is hard, justifying the use of meta-learning algorithms for metaheuristic recommendation. These meta-learning algorithms use past experience to choose the best metaheuristic to solve an unseen
doi:10.1109/bracis.2018.00037
dblp:conf/bracis/MirandaFNFO18
fatcat:nxh36vg6ojbqhazbu3ftib7oui