Machine Learning and Optimization

Machine Learning and Optimization Research Group

Scientific research of our group focuses on the development of modern algorithms of artificial intelligence. We are particularly interested in methods of machine learning and optimization using nature-inspired metaheuristics. More detailed areas of our research are:

  • Deep learning and neural networks
  • Face recognition with local patterns
  • Ensemble classification
  • Evolutionary and memetic computation
  • Multiobjective optimization
  • Game theory in machine learning and optimization
  • Fuzzy methods for time series analysis
  • Data intensive computing


Leader: dr hab. inż. Michał Bereta

Scopus Author ID: 36757629000




dr hab. Joanna Kołodziej, Professor at the Cracow University of Technology

Cracow University of Technology

NASK – Research and Academic Computer Network, Warsaw

dr inż. Paweł Jarosz

Cracow University of Technology

dr Adam Marszałek

Cracow University of Technology

Research projects

cHiPSet (COST Horizon2020 IC1406) –

BalticSatApps (InterReg)


M. Bereta, “Regularization of boosted decision stumps using tabu search,” Applied Soft Computing Journal, vol. 79, pp. 424–438, 2019.
J. Kolodziej and H. González-Vélez, Eds., High-Performance Modelling and Simulation for Big Data Applications – Selected Results of the {COST} Action {IC1406} cHiPSet, vol. 11400. Springer, 2019.
M. Bereta, “Baldwin effect and Lamarckian evolution in a memetic algorithm for Euclidean Steiner tree problem,” Memetic Computing, vol. 11, no. 1, pp. 35–52, 2019.
A. Bazan-Krzywoszańska, M. Bereta, “The use of urban indicators in forecasting a real estate value with the use of deep neural network”, Reports on Geodesy and Geoinformatics, Vol 106, pp. 25-34, 2018.
S. Memeti, S. Pllana, A. P. D. Binotto, J. Kolodziej, and I. Brandic, “A Review of Machine Learning and Meta-heuristic Methods for Scheduling Parallel Computing Systems,” in Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, {LOPAL} 2018, Rabat, Morocco, May 2-5, 2018.
M. Bereta, “Monte Carlo Tree Search Algorithm for the Euclidean Steiner Tree Problem,” Journal of Telecommunications and Information Technology, no. 4, pp. 71–81, 2017.
M. Bereta, “Entropy-based regularization of AdaBoost,” Computer Assisted Methods in Engineering and Science, vol. 24, no. 2, pp. 89–100, 2017.
J. Zhao et al., “Trusted Performance Analysis on Systems With a Shared Memory,” {IEEE} Systems Journal, vol. 11, no. 1, pp. 272–282, 2017.
J. Kolodziej, H. González-Vélez, and H. D. Karatza, “High-performance modelling and simulation for big data applications,” Simulation Modelling Practice and Theory, vol. 76, pp. 1–2, 2017.
M. Iacono, M. Gribaudo, J. Kolodziej, and F. Pop, “Modeling and evaluation of highly complex computer systems architectures,” J. Comput. Science, vol. 22, pp. 126–130, 2017.
L. Vasiliu, F. Pop, C. Negru, M. Mocanu, V. Cristea, and J. Kolodziej, “A Hybrid Scheduler for Many Task Computing in Big Data Systems,” Applied Mathematics and Computer Science, vol. 27, no. 2, p. 385, 2017.