Hybridizing the dynamic mutation approach with local searches to overcome local optima

A Memetic Algorithm is an Evolutionary Algorithm augmented with local searches. The dynamic mutation approach has been studied extensively in experiments of Memetic Algorithms, but only a few studies in theory. We previously defined a metric BLOCKONES to estimate the difficulty of escaping from a local optima, and showed that the algorithm´s ability of escaping from a local optima, that has a large BLOCKONES, is very important, because it dominates the time complexity of finding a global optimal solution. In this paper, we will use the same metric and show the benefits of hybridizing the dynamic mutation approach with one of two local searches, best-improvement and first-improvement. In short, this hybridization greatly enhances the algorithm´s ability to escape from any local optima.