A new technique for dynamic size populations in genetic programming

New techniques for dynamically changing the size of populations during the execution of genetic programming systems are proposed in this paper. Two models are presented, allowing to add and suppress individuals on the basis of some particular events occurring during the evolution. These models allow to find solutions of better quality, to save considerable amounts of computational effort and to find optimal solutions more quickly, at least for the set of problems studied here, namely the artificial ant on the Santa Fe trail, the even parity 5 problem and one instance of the symbolic regression problem. Furthermore, these models have a positive effect on the well known problem of bloat and act without introducing additional computational cost.