This paper explores the use of a global contrast factor (GCF) as an aesthetic measure to aid the generation of fractal landscapes. In an attempt to auto generation virtual landscapes, we added a global contrast factor as an aesthetic measure based fitness function to the genetic algorithm (GA). This GA is used to explore a multi-dimensional parameter space that defines how 3D fractal landscapes are created. Two types of experiments were conducted using GCF that facilitated fluid evaluation of computationally intensive fitness evaluation, with preliminary results reported.

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This article examines whether textural generation system imagery evolved with computational aesthetic support can be judged as having aesthetic attributes, both when knowing and not knowing its true origin. Such a generation, depicting a digital landscape, is offered to two groups of participants to appraise. It is hypothesized that there will be no statistically significant difference between the groups on their appraisal of the image. Results from statistical analysis prove to be consistent with this hypothesis. A minority of participants, however, do exhibit significant differences in their perception of the image based on its means of production. This article explores and illustrates these differences.