A robust memetic algorithm with self-stopping capabilities

Evolutionary algorithms exhibit some traditional handicaps: lack of a stopping criterion, slow convergence towards the minimum, etc. Memetic algorithms try to combine the best exploration qualities of population based approaches with the exploitation qualities of local search ones. The proposed solution in this work, Robust Evolutionary Strategy Learned with Automated Termination Criteria (R-ESLAT) uses a memetic approach, combining an evolutionary strategy with derivative-free local search methods, adding as well a termination criteria based on the population diversity, according to the principles of the original ESLAT algorithm. The original algorithm is analyzed and its features improved towards an increased robustness, comparing the results obtained with the Covariance Matrix Adaptation Evolutionary Strategy (CMAES).