We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.

Utilization of computational approach in the study of social behaviour of animals is increasing and we attempted such an approach in our study of tree-dwelling bats. These bats live in highly dynamic fission–fusion societies that share multiple roosts in a common home range. The key behavioural component associated with complex and non-centralized decision-making processes in roost switching is swarming around potential locations in order to recruit members to the new roost. To understand roost switching dynamics of bat groups in their natural environment, we employed a computational model, the SkyBat, which is based on swarm algorithm, to model this process. In a simulated environment of this agent-based model, we replicated natural fission–fusion dynamics of the Leisler’s bat, Nyctalus leisleri, groups according to predefined species and habitat parameters. Spatiotemporal patterns of swarming activity of agents were similar to bats. The number of simulated groups formed prior to sunrise, the mean number of individuals in groups and the roost height did not differ significantly from data on a local population of bats collected in the field. Thus, the swarm algorithm gave a basic framework of roost-switching, suggesting possible applications in the study of bat behaviour in rapidly changing environments as well as in the field of computer science.

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Insect colony inspires scientists for years to create similar behavior in the robotic application. The main goal of our work was to develop simple and powerful algorithm which will accept dynamically changes in the size of a robot swarm due the mission. This algorithm is suitable for situations where unpredictable conditions may lead to robot fault in multi-robotics system and mission completion is endangered. In this article we would like to investigate properties of a simple pheromone based algorithm. The algorithm operates as cellular automata and partially uses an insect pheromone strategy for the robots coordination. Our abstract model is a decentralized adaptive system with a shared memory which represents the environment.