This paper introduces the Auto Terrain Generation System (ATGS), which is based on an Interactive Genetic Algorithm (IGA) that enables non-specialist users to rapidly generate terrains. The motivation for using an IGA is discussed, existing terrain generation techniques are described and a new approach, based on a fractal terrain engine, is outlined. Graphics engines allow terrains to be specified with over 800 floating point parameters, which can overwhelm nonspecialist users. These parameters also create a vast search space for auto-terrain generation systems that can complicate procedural techniques. ATGS addresses these issues via interaction with the user, which is implemented in ATGS by a web based user interface that allows users to rapidly indicate terrain preferences. These user preferences are used by a genetic algorithm to explore a multi-dimensional parameter space that satisfies the user's intuition and aesthetics. Proof of concept experiments are outlined, results are presented and future research work is projected.

This paper explores the use of an aesthetic measure to aid the generation of fractal landscapes. Virtual landscapes are important for applications ranging from games to simulation. This paper extends work done on the auto generation of virtual landscapes for climate change visualisation, by adding an aesthetic measure based fitness function to the evolutionary algorithm, thus reducing the reliance of the method on user based evaluation. A genetic algorithm that uses an aesthetic measure of fitness based on information theory is defined. This GA is used to explore a multi-dimensional parameter space that defines how 3D virtual landscapes are created. The utility of this fitness measure is assessed by evaluating the solutions generated by the system with real users. Results indicate that genetic algorithms that use information theory based fitness measures do indeed generate virtual artefacts that match user preferences. Moreover the images generated are visually appealing enough to be curated for public exhibition along side human artists in art galleries by professional art critics.

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.

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.