Region based memetic algorithm with LS chaining
Benjamin Lacroix, Daniel Molina, Francisco Herrera · 2012
Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimisation. That implies the evolutionary algorithm component should be focused in exploring the search space while the local search method exploits the achieved solutions. In a previous work, it was proposed a memetic algorithm, MA-LSCh-CMA, that was able to work with a local search method, CMA-ES, with a great exploitation factor, but without a mechanism to maintain diversity and avoid competition between the evolutionary algorithm and CMA-ES. In this work, we propose a variation of this algorithm, called RMA-LSCh-CMA, adding a niching strategy that divide the domain search in equal hypercubes. The experimental results obtained show that the new version is statistically better than the previous one and is very competitive in comparisons with the state-of-the-art algorithm IPOP-CMA-ES, obtaining equivalent results on the medium and higher dimensions, although slightly better in the higher dimension.