Perhaps the best known example of user-guided evolution is furnished by evolving expressions, an image generation technique first introduced by Sims. In this version of artificial evolution, images are evolved for aesthetic purposes, hence any fitness measure used must be based on aesthetics. We consider the problem of guiding image evolution autonomously on the basis of computational, as opposed to user-assigned, aesthetic fitness. Due to the difficulty of formulating an absolute criterion for aesthetic fitness, we adopt a coevolutionary approach, relying on hosts and parasites to establish relative criteria for aesthetic fitness. To sustain the coevolutionary arms race, we allow coevolution to proceed in stages. This permits appropriate fitness levels to be maintained within the parasite populations we use to infect host image populations. Using staged coevolution produces two beneficial results: (1) longer survival times for subpopulations of host images, and (2) stable phenotypic lineages for host images.
Texture Image Generation Technique Considering Storage Optimization of 3D-Spatial Data