Cellular Automata Model of Macroevolution

In this paper I describe a cellular automaton model of a multi-species ecosystem, suitable for the study of emergent properties of macroevolution. Unlike majority of ecological models, the number of coexisting species is not fixed. Starting from one common ancestor they appear by "mutations" of existent species, and then survive or extinct depending on the balance of local ecological interactions. Monte-Carlo numerical simulations show that this model is able to qualitatively reproduce phenomena that have been observed in other models and in nature.Comment: 8 pages, 3 figures, Fourteenth National Conference on Application of Mathematics in Biology and Medicine, Leszno 2008 (POLAND

A multiresolution approach based on MRF and Bak-Sneppen models for image segmentation

The two major Markov Random Fields (MRF) based algorithms for image segmentation are the Simulated Annealing (SA) and Iterated Conditional Modes (ICM). In practice, compared to the SA, the ICM provides reasonable segmentation and shows robust behavior in most of the cases. However, the ICM strongly depends on the initialization phase. In this paper, we combine Bak–Sneppen model and Markov Random Fields to define a new image segmentation approach. We introduce a multiresolution technique in order to speed up the segmentation process and to improve the restoration process. Image pixels are viewed as lattice species of Bak–Sneppen model. The a-posteriori probability corresponds to a local fitness. At each cycle, some objectionable species are chosen for a random change in their fitness values. Furthermore, the change in the fitness of each species engenders fitness changes for its neighboring species. After a certain number of iteration, the system converges to a Maximum A Posteriori estimate. In this multireolution approach, we use a wavelet transform to reduce the size of the system