In this work, we develop a new paradigm, called Meta-Evolutionary Algorithms, motivated by the challenging, continuous problems encountered in the domain of satisfiability in fuzzy logics (SAT∞). In Meta-Evolutionary Algorithms, the individuals in a population are optimization algorithms them-selves. Mutation at the meta-population level is handled by performing an optimization step in each optimization algorithm, and recombination at the meta-population level is handled by exchanging information between different algorithms. We analyse different recombination operators and empirically show that simple Meta-Evolutionary Algorithms are able to outperform CMA-ES on a set of SAT∞ benchmark problems.