EVOLUTIONARY ALGORITHMS LEARNING METHODS IN STUDENT EDUCATION

Authors

  • Pēteris Grabusts Rezekne Academy of Technologies
  • Alex Zorins Rezekne Academy of technologies

DOI:

https://doi.org/10.17770/sie2021vol5.6153

Keywords:

data analysis, evolutionary algorithms, genetic algorithms, modelling, teaching

Abstract

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.

 

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References

Bot, M.C., & Langdon, W.B. (2000). Application of Genetic Programming to Induction of Linear Classification Trees. Proceedings of the Third European Conference on Genetic Programming. Received from: https://link.springer.com/chapter/10.1007/978-3-540-46239-2_18

Buontempo, F. (2019). Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions. The Pragmatic Programmers, 225p.

Goldberg, D. (1988). Genetic Algorithms in Search, Optimization and Machine Learning. 13th ed. Edition. Addison-Wesley Professional, 432p.

Grabusts, P. (2009). Evolutionary algorithms at choice: From GA to GP. 7th International Scientific and Practical Conference “Environment, Technology and Resources”, Rezekne. Volume 2, 2009, 185-192.

Gupta, S., & Sinha, S. (2020). Academic Staff planning, allocation and optimization using Genetic Algorithm under the framework of Fuzzy Goal Programming. Procedia Computer Science, 172. Retrieved from: https://www.sciencedirect.com/science/article/pii/S1877050920314599

Haupt, R.L., & Haupt S.E. (2004). Practical Genetic Algorithms. John Wiley & Sons.

Introduction on Evolutionary Algorithms. (2011). Retrieved from: https://neo.lcc.uma.es/opticomm/introea.html

Karr, C., & Freeman, L.M. (1999). Industrial Applications of Genetic Algorithms. International Series on Computational Intelligence: CRC Press.

Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge: MIT Press.

Mitchell, M. (1999). An introduction to Genetic Algorithms. A Bradford Book. The MIT Press.

Hemert, J.I. (1998). Applying Adaptive Evolution Algorithms to Hard Problems. Master Thesis. Leiden University.

Weise, T. (2009). Global Optimization Algorithms - Theory and Application. Retrieved from: http://www.it-weise.de/projects/book.pdf

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Published

2021-05-28

How to Cite

Grabusts, P., & Zorins, A. (2021). EVOLUTIONARY ALGORITHMS LEARNING METHODS IN STUDENT EDUCATION. SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference, 5, 330-339. https://doi.org/10.17770/sie2021vol5.6153