Satisfiability solving in fuzzy logics is a subject that has not been researched much, certainly compared to satisfiability in propositional logics. Yet, fuzzy logics are a powerful tool for modelling complex problems. Recently, we proposed an optimization approach to solving satisfiability in fuzzy logics and compared the standard Covariance Matrix Adaptation Evolution Strategy algorithm (CMA-ES) with an analytical solver on a set of benchmark problems. Especially on more finegrained problems did CMA-ES compare favourably to the analytical approach. In this paper, we evaluate two types of hillclimber in addition to CMA-ES, as well as restart strategies for these algorithms. Our results show that a population-based hillclimber outperforms CMA-ES on the harder problem class.
Additional Metadata
THEME Logistics (theme 3)
Publisher IEEE
Editor R. Alcalá , Y. Nojima
Persistent URL dx.doi.org/10.1109/GEFS.2013.6601055
Conference IEEE Symposium Series on Computational Intelligence
Citation
Brys, T, Drugan, M.M, Bosman, P.A.N, de Cock, M, & Nowé, A. (2013). Local Search and Restart Strategies for Satisfiability Solving in Fuzzy Logics. In R Alcalá & Y Nojima (Eds.), Proceedings of IEEE Symposium Series on Computational Intelligence 2013 (pp. 52–59). IEEE. doi:10.1109/GEFS.2013.6601055