Local Search and Restart Strategies for Satisfiability Solving in Fuzzy Logics
Presented at the IEEE Symposium Series on Computational Intelligence
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.
|R. Alcalá , Y. Nojima|
|IEEE Symposium Series on Computational Intelligence|
|Organisation||Intelligent and autonomous systems|
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