This paper proposes a new framework for non-convex optimization referred to as Metacyclic Parameter Search (MEPS). The framework combines several approaches that are well known from the field of artificial intelligence-namely the iterative update of generations of candidate solutions prevalent in evolutionary computing and particle swarm optimization, as well as metacognitive approaches implementing reward-based improvement such as (deep) reinforcment learning-resulting in a gradient-free approach to non-convex optimization that combines the benefits (and alleviates the mutual shortcomings) of each of these individual approaches. Following an overview of the framework, its workings are demonstrated on three rudimentary examples.