Population Migration Using Dominance in Multi-population Cultural Algorithms

Abstract

In this study we introduce a new method to enable the migration of individuals from one population to another using the concept of dominance in Multi-Population Cultural Algorithms (MPCA's). The MPCA's artificial population comprises of agents that belong to a certain sub-population. Multiple sub-populations are generated, each running its own Cultural Algorithm (CA). In this work we create a dominance-MPCA (D-MPCA) with a network of populations that implements a dominance strategy. We hypothesize that the evolutionary advantage of dominance can help improve the performance of MPCA in general optimization problems. The Sphere function from the CEC 2013 benchmark optimization functions is used to calculate the fitness value of the individuals. We observe how the populations adapt to the changes. Preliminary results show improved performance in our proposed D-MPCA over traditional MPCA.