Real time terrain generation is a vital part in the development of realistic computer simulations and games. Dynamic terrain generation influences the realism of simulation, because its participants have to adapt to the current environment conditions. Dynamically generated primary terrain is transformed in order to reflect natural phenomena, such as thermal and water erosion, avalanches or glaciers. In this article a possibility of primary terrain transformation with application of artificial neural networks is shown. The networks are trained by evolutionary algorithms to solve a problem of a water erosion phenomenon. Obtained results show that application of such neural networks to this problem can significantly reduce the processing time needed to perform the process of modeling the natural phenomena.
Artificial neural networks are successfully applied not only for solving problems which cannot be described by mathematical formulae, but also for optimization of existing solutions. Interesting possibility for building proper neural networks can be application of evolutionary algorithms. There are three methods of preparing neural networks discussed in this article: weight-based, neuron-based and path-based adaptation. What is more, the article discusses behavior of the genetic algorithm depending on different methods of removing weights in the network.