Maintaining population diversity in evolutionary art using structured populations

Maintaining population diversity is an important and difficult task in Evolutionary Computation in general and Evolutionary Art in particular. A lack of population diversity will result in inefficient search behaviour and premature convergence. In this paper we investigate the effect of using spatially structured populations on population diversity in Evolutionary Art. To this end, we perform several experiments with unsupervised evolution (no human in the loop) of aesthetically pleasing images using a panmictic model Evolutionary Algorithm, a distributed Island Model (with a Best-First selection scheme and with the Multikulti algorithm) and a Cellular Evolutionary Algorithm. In our Island Models experiments we use a number of different parameters settings for number of islands, island size, migration interval, migration size, and initialisation methods. In our Cellular EA experiments we use different settings for width, height and neighbourhood. We also compare the use of structured populations with the use of a panmictic EA with enhanced genetic operators. We find that the use of structured populations is beneficial for maintaining both phenotype and genotype diversity. All configurations of Island Models and Cellular EA outperform our standard panmictic EA on population diversity.