Peter A. Siepmann

Learn More
Cellular automata (CA) are an important modelling paradigm in the natural sciences and an extremely useful approach in the study of complex systems. Homogeneity, massive parallelism, local cellular interactions and both synchronous and asynchronous models of rule execution are some of their most prominent features, allowing scientists to model and(More)
Pseudomonas aeruginosa is an opportunistic bacterium that exploits quorum sensing communication to synchronize individuals in a colony and this leads to an increase in the effectiveness of its virulence. In this paper we derived a mechanistic P systems model to describe the behavior of a single bacterium and we discuss a possible approach, based on an(More)
We present a new methodology, based on a combination of genetic algorithms and image morphometry, for matching the outcome of a Monte Carlo simulation to experimental observations of a far-from-equilibrium nanosystem. The Monte Carlo model used simulates a colloidal solution of nanoparticles drying on a solid substrate and has previously been shown to(More)
Natural world processes have, for nearing fifty years, been the inspiration for a number of artificial intelligence methods [Hir00, Koz92]. In this project, the model of Darwinian evolution will be used to develop a system that, through combining distributed processing technologies with evolutionary population-and search-based algorithms, can be used to(More)
  • 1