A robust experimental evaluation of the co-evolutionary islands model

Abstract

The island model is a common framework for parallel and distributed evolutionary algorithms. This framework that is closely connected to memetic computing, since it studies how multiple search processes with different operators exchange information, preserve diversity, and collectively improve candidate solutions. In this model, we have two or more islands, and every island contains a different population that evolves semi-isolated from the others, exchanging solutions among them periodically through a migration operator. This work performs a robust and extensive evaluation of this model from a memetic-computing perspective, emphasizing cooperation, heterogeneity, and knowledge transfer among search components. We evaluate implementations ranging from two to fifty islands. Furthermore, we evaluate this model using five different evolutionary algorithms: the genetic algorithm, the ant colony optimization, the particle swarm optimization, the differential evolution, and the CLONAL algorithm, where each island can be evolved by any of these five algorithms, allowing for homogeneous and heterogeneous configurations. This evaluation is carried out using the 2015 IEEE Congress of Evolutionary competition on learning-based real-parameter single objective optimization as test functions. We optimized 49 different co-evolutionary island models using the five chosen evolutionary algorithms, whereas each co-evolutionary algorithm was developed using a different number of islands. A statistical analysis of our experimental data demonstrated that the use of several islands is beneficial to the results of the evolutionary algorithms. Indeed, we were able to show that increasing the number of islands generally leads to solutions closer to the optimum, especially when islands combine complementary search behaviors.