The effects of varying population density in a fine-grained parallel genetic algorithm

This paper introduces a new method for controlling selection pressure in fine-grained parallel GAs. Our model, inspired by percolation theory, employs a "seeding" mechanism, which provides a means of systematically increasing the population size until the carrying capacity of the lattice is reached. Initially, a relatively small number of individuals (solutions) occupy small isolated patches (demes). As time goes by, additional randomly generated individuals are added to the lattice. As the density increases, the small isolated demes gradually merge to form larger connected demes. This "percolation process" helps to balance the interplay between genetic and population forces. The implications of alternative migration schemes between demes are also investigated in terms of the population diversity, selection pressure and consequently algorithm performance. Experimental results using benchmark optimisation problems confirm that the "step-wise" increase in the population density does affect the quality of the solutions found in a given trial