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  • Publication
    Teaching experience shows that during educational process student perceive graphical information better than analytical relationships. As a possible solution, there could be the use of package Matlab in realization of different algorithms for IT studies. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic, clustering and evolution methods. Series of research were carried out in order to demonstrate the suitability of the Matlab for the purpose of visualization of various simulation models of some data mining disciplines – particularly genetic algorithms. Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. There are four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyses present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. Genetic algorithm learning methods are often undeservedly forgotten, although the implementation of their algorithms is relatively strong and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies were demonstrated based on genetic algorithms and real examples. We assume that students already have prior knowledge of genetic algorithms.
  • Publication
    The teaching experience in the study process shows that students are better at perceiving graphical information rather than analytical relationships. Many training courses run on models that were previously available only in mathematics or physics. The use of Matlab package for the implementation of various algorithms in the Information Technology fields could be a possible solution. Often, the analytical solution is much simpler than the visual Matlab model, but for the purposes of perspective training it gives understanding of the usefulness of using such models. In the previous articles the authors had given examples of how Matlab's possibilities could be used for economic research purposes (optimal tax rate searching and modelling market equilibrium price). Students are very interested in modern data mining methods, such as artificial neural networks. In the research part of the study, the modelling capabilities in data mining studies are demonstrated by neural network examples.