Dynamically updated region based memetic algorithm for the 2013 CEC Special Session and Competition on Real Parameter Single Objective Optimization

Abstract

In this paper, we present a memetic algorithm which combines in a local search chaining framework, a steady-state genetic algorithm as evolutionary algorithm and a CMA-ES as local search method. It is an extension of an already presented algorithm which uses a region-based niching strategy and which has proven to be very efficient on real parameter optimisation problems. In this new version, we propose to dynamically update the niche size in order to make it less dependent to such critical parameter. In addition, we used an automatic configuration tool to optimise its parameters, and show that the optimised version of this algorithm is significantly better than with its default parameters. We tested this algorithm on the Special Session and Competition on Real-Parameter Optimization of the IEEE Congress on Evolutionary 2013 benchmark.