DOMINANCE IN MULTI-POPULATION CULTURAL ALGORITHMS

We propose a new approach that can be used for solving the knowledge migration issue in multi-population cultural algorithms (MPCA). In this study we introduce a new method to enable the migration of individuals from one population to another using the concept of complete dominance applied to MPCA. The MPCA’s artificial population comprises of agents that belong to a certain sub-population. In this work we create a dominance multi population cultural algorithm (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. Three benchmark optimization functions are used to calculate the fitness value of the individuals. The proposed D-MPCA showed improved performance over the traditional MPCA. We conclude that dominance helps in improving the efficiency of knowledge migration in MPCA.