Initializing the population is a crucial step for genetic programming, and several strategies have been proposed so far. The issue is particularly important for geometric semantic genetic programming, where initialization is known to play a very important role. In this paper, we propose an initialization technique inspired by the biological phenomenon of demes despeciation, i.e. the combination of demes of previously distinct species into a new population. In synthesis, the initial population of geometric semantic genetic programming is created using the best individuals of a set of separate subpopulations, or demes, some of which run standard genetic programming and the others geometric semantic genetic programming for few generations. Geometric semantic genetic programming with this novel initialization technique is shown to outperform geometric semantic genetic programming using the traditional ramped half-and-half algorithm on six complex symbolic regression applications. More specifically, on the studied problems, the proposed initialization technique allows us to generate solutions with comparable or even better generalization ability, and of significantly smaller size than the ramped half-and-half algorithm.
