9600-0009 – Unconvential Algorithms and Computations (NAV)

The aim of the course is to acquaint its students with unconventional algorithms from physical, biological processes and complex systems. The graduate will gain an overview of modern computational procedures based on principles observed from complex processes and dynamics. Upon successful completion of the course, the graduate will be able to apply the methods discussed in the course to real problems. This course is not a free continuation of the BIA course.

The aim of the lectures is to introduce to the students the problems of non-conventional algorithms, their biological - physical origin. The lectures will be discussed the various areas of their origin, usually from natural complex systems with an emphasis of their physical-mathematical and algorithmic description and implementation of the PC. The lectures will give the students an interdisciplinary perspective on the issue of non-conventional algorithms, complex systems and their dynamic behaviour. Students get an overview of modern computational algorithms allowing them to model and simulate the otherwise very complicated and complex systems (deterministic chaos, Thom's catastrophe theory, fractal geometry, swarm intelligence, algorithms, quantum mechanics, cellular automata, "physarium machines", "Self-Organized criticality", ...) and vice-versa will get insight on how are unconventional algorithms derived from above-mentioned complex systems. After successfully completing the course will have an interdisciplinary graduate survey knowledge of unconventional algorithms and will be able to apply the methods discussed in the course to real problems. Students should be able to continue further in deeper self-study in this topic.

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4. Bekenstein J. D., Information in the Holographic Universe, Scientific American, August, 2003 5. R. Gilmore 1993, Catastrophe Theory for Scientists and Engineers, John Wiley and Sons, ISBN 0-486-67-539-4, 1993 6. Gheorghe Paun (Author), Grzegorz Rozenberg (Author), Arto Salomaa, DNA Computing: New Computing Paradigms, Springer, ISBN 978-3540641964 7. Zelinka I, Celikovsky S, Richter H and Chen G., (2010) Evolutionary Algorithms and Chaotic Systems, (Eds), Springer, Germany, 550s, 2010.