doc. Ing. Radomil Matoušek, Ph.D.

E-mail:   matousek@fme.vutbr.cz 
WWW:   http://www.uai.fme.vutbr.cz/~matousek/
Dept.:   Institute of Automation and Computer Science
Position:   Director of Institute
Room:   A3/720
Phone:   +420 54114 2298
Dept.:   Institute of Automation and Computer Science
Dept. of Applied Computer Science
Position:   Head of Department
Room:   A3/720
Phone:   +420 54114 2298
Dept.:   Institute of Automation and Computer Science
Dept. of Applied Computer Science
Position:   Associate Professor
Room:   A4/203
Phone:   +420 54114 2288

2401

Prizing by scientific community

  • Prices:
  • 2000 Price for the Best Paper of the Euro-International Symposium on Computational Intelligence
  • 2004 Session Best Presentation Award (SCIS&ISIS 2004, Yokohama, Japan)
  • Invited lectures:
  • 2000: Germany, Zittau, Fuzzy Coloquium, Prof. R. Hampel, University of Applied Science Zittau / Gorlitz, Téma: Fuzzy Setting of GA Parameters.
  • 2004: Japan, Kyoto, Department of Energy Conversion Science, Prof. E. Matsumoto, Kyoto University, Faculty of Energy Science Téma: Non-linear regression by means of GAFIS.
  • )

Sum of citations (without self-citations) indexed within SCOPUS

127

Supervised courses:

Publications:

  • MATOUŠEK, R.; ŠOUSTEK, P.; DVOŘÁK, J.; BEDNÁŘ, J.:
    ACO in Task of Canadian Treveller Problem,
    18th International Conference of Soft Computing, MENDEL 2012 (id 19255), pp.600-603, ISBN 978-80-214-4540-6, (2012), VUT
    conference paper
    akce: 18th International Conference on Soft Computing, MENDEL 2012, Brno University of Technology, 27.06.2012-29.06.2012
  • MATOUŠEK, R.; MINÁŘ, P.; LANG, S.; ŠEDA, M.:
    HC12: Efficient PID Controller Design,
    Engineering Letters, Vol.20, (2012), No.1, pp.42-48, ISSN 1816-093X, International Association of Engineers
    journal article
  • MATOUŠEK, R.; MINÁŘ, P.; LANG, S.; PIVOŇKA, P.:
    HC12: Efficient Method in Optimal PID Tuning,
    Lecture Notes in Engineering and Computer Science, Vol.2011, (2011), No.1, pp.463-468, ISSN 2078-0958, Newswood Limited
    journal article
  • MATOUŠEK, R.; ŽAMPACHOVÁ, E.:
    Promising GAHC and HC12 algorithms in global optimization tasks,
    OPTIMIZATION METHODS & SOFTWARE, Vol.26, (2011), No.3, pp.405-419, ISSN 1055-6788, Taylor & Francis
    journal article
  • MATOUŠEK, R.:
    HC12 Implemented Using the CUDA Platform,
    Applied Computer Science, Vol.2010, (2010), No.2010, pp.649-652, ISSN 1792-4863, WSEAS Press
    journal article
    akce: International Conference on Applied Computer Science (ACS), Malta, 15.09.2010-17.09.2010
  • MATOUŠEK, R.:
    GAHC: Improved Genetic Algorithm,
    Studies in Computational Intelligence, Vol.2008, (2008), No.129, pp.507-520, ISSN 1860-949X, Springer
    journal article
  • MATOUŠEK, R.:
    GAHC: Improved GA with HC mutation,
    WCECS 2007, pp.915-920, ISBN 978-988-98671-6-4, (2007), Newswood Limited
    conference paper
    akce: World Congress on Engineering and Computer Science 2007, San Francisco, 24.10.2007-26.10.2007
  • Zdeněk NĚMEC, Radomil MATOUŠEK, Ladislav BÉBAR, Tomáš PAŘÍZEK:
    AUTOMATIC CONTROL OF NITROGEN OXIDES REDUCTION DURING WASTE COMBUSTION,
    CHISA 2006, pp.P5.082-4, ISBN 80-86059-45-6, (2006), Process Engineering Publisher, Praha, Czech republik
    conference paper
    akce: 17th International Congress of Chemical and Process Engineering CHISA 2006, Praha, 27.08.2006-31.08.2006
  • MATOUŠEK, R.:
    ELITE TOURNAMENT SELECTION,
    MENDEL 2005, pp.211-216, ISBN 80-214-2961-5, (2005), Brno University of Technology
    conference paper
  • MATOUŠEK, R.:
    GAFIS: Genetic Algorithm with Fuzzy Inference Systém,
    SCIS & ISIS 2004, pp.WP-2-3-5, (2004), Keio University
    conference paper
    akce: SCIS&ISIS2004 (Joint 2nd International Conference on Soft Computing and Intelligent Systems and 5th International Symposium on Advanced Intelligent Systems), Yokohama, Japan, 21.09.2004-24.09.2004
  • MATOUŠEK, R., OŠMERA, P., ROUPEC, J., ŠEDÁ, J.:
    Adaptive Genetic Algorithms Based on Fuzzy Inference System,
    Intelligent Computing and Information Systems, pp.136-141, ISBN 977-237-172-3, (2002), Nubar Printing House
    conference paper
    akce: First International Conference on Intelligent Computing and Information Systems ICICIS 2002., Cairo, 24.06.2002-26.06.2002
  • POPELA, P., ROUPEC, J., OŠMERA, P., MATOUŠEK, R.:
    The Formal Stochastic Framework for Comparison of Genetic Algorithms,
    The 2002 IEEE World Congress on Computational Intelligence, pp.576-581, ISBN 0-7803-7281-6, (2002), IEEE
    conference paper
    akce: The 2002 IEEE World Congress on Computational Intelligence, Honolulu, Hawaii, 12.05.2002-17.05.2002
  • MATOUŠEK, R., OŠMERA, P.:
    Design of adaptive genetic algorithms based on fuzzy inference system,
    9th Zittau Fuzzy Colloquium 2001, pp.201-205, ISBN 3-9808089-0-4, (2001), Hochshule Zittau/Goerlitz
    conference paper
    akce: 9th Zittau Fuzzy Colloquium 2001, Zittau, 17.09.2001-19.09.2001
  • PRAX, P., MATOUŠEK, R., MENŠÍK, M.:
    Projekt využití evolučních algoritmů pro vyhodnocení spolehlivosti a rizik odvodnění urbanizovaného území,
    Odpadní vody - WASTEWATER 2001, pp.285-290, ISBN 80-238-6917-5, (2001), AČE ČR
    conference paper
    akce: 4. Mezinárodní bienální konference a výstava Odpadní vody 2001, Mladá Boleslav, 15.05.2001-17.05.2001
  • MATOUŠEK, R., OŠMERA, P.:
    Fuzzy Setting of GA Parameters,
    Advances in Soft Computing – Fuzzy Control Theory and Practice, pp.302-312, ISBN 3-7908-1327-3, (2000), Physica –Verlag, A Springer - Verlag Company
    book chapter
  • MATOUŠEK, R., OŠMERA, P., ROUPEC, J.:
    GA-FIS for Dynamic Environment,
    The European Symposium on Computational Intelligence, pp.191-196, ISBN 3-7908-1322-2, (2000), Physica –Verlag, A Springer-Verlag Company
    conference paper
  • OŠMERA, P., ROUPEC, J., MATOUŠEK, R.:
    Genetic Algorithms with Diploid Chromosomes and Sexual Reproduction,
    Quo Vadis Computational Intelligence – new Trends and Approaches in Computational Intelligence, pp.317-323, ISBN 3-7908-1324-9, (2000), Physica –Verlag, Springer - Verlag Company
    book chapter
  • MATOUŠEK, R., OŠMERA, P., ROUPEC, J.:
    GA with Fuzzy Inference System,
    2000 Congress on Evolutionary Computation, pp.646-651, ISBN 0-7803-6375-2, (2000), IEEE Service Center
    conference paper
    akce: Congress on Evolutionary Computation, La Jolla, California, USA, 16.07.2000-19.07.2000

List of publications at Portal BUT

Abstracts of most important papers:

  • MATOUŠEK, R.; LANG, S.; MINÁŘ, P.; PIVOŇKA, P.:
    Evolutionary Design of Polynomial Controller,
    World Academy of Science, Engineering and Technology, Vol.2011, (2011), No.59, pp.639-644, ISSN 2010-376X, WASET
    journal article

    In the control theory one attempts to find a controller that provides the best possible performance with respect to some given measures of performance. There are many sorts of controllers e.g. a typical PID controller, LQR controller, Fuzzy controller etc. In the paper will be introduced polynomial controller with novel tuning method which is based on the special pole placement encoding scheme and optimization by Genetic Algorithms (GA). The examples will show the performance of the novel designed polynomial controller with comparison to common PID controller.
  • MATOUŠEK, R.; ŽAMPACHOVÁ, E.:
    Promising GAHC and HC12 algorithms in global optimization tasks,
    OPTIMIZATION METHODS & SOFTWARE, Vol.26, (2011), No.3, pp.405-419, ISSN 1055-6788, Taylor & Francis
    journal article

    This paper deals with a new stochastic heuristic searching algorithm inspired by the fundamental biological principles of survival. It presents a very promising version of a commonly known genetic algorithm denoted as GAHC and an algorithm denoted as HC12. Global optimization properties of these algorithms are illustrated with several nonlinear optimization problems. These problems are also solved by sophisticated solvers in general algebraic modelling system to increase objectivity and to compare different methods. Presented optimization algorithms are implemented in our own optimization toolbox GATE in Matlab environment.
  • MATOUŠEK, R.; KARPÍŠEK, Z.:
    Exotic Metrics for Function Approximation,
    17th International Conference of Soft Computing, MENDEL 2011 (id 19255), pp.560-566, ISBN 978-80-214-4302-0, (2011), VUT
    conference paper
    akce: 17th International Conference of Soft Computing, MENDEL 2011, Brno University of Technology, 15.06.2011-17.06.2011

    In technical practice we are very often confronted with need to approximate functions from measured values. Another frequent task is a calculation of measure of central tendency of sample data. For a good reason the method of least squares and the statistics like mean or median are being used. The goal of this paper is to show some nonstandard metrics usable in tasks of creation of approximation model or in tasks of symbolic regression. These metrics, as will be shown, can be created using so-called generating function. It is important to note these metrics can affect robustness of created model concerning extremely deviated values. Using these exotic metrics in tasks of data approximation or symbolic regression we get nonlinear unconstrained optimization task. To solve such task it is necessary to use adequate optimization strategies such as soft-computing methods (evolution algorithms, HC12, differential evolution, etc.) or classical methods of nonlinear optimization (Nelder-Mead, conjugate gradient, Levenberg–Marquardt algorithm, etc.).
  • MATOUŠEK, R.:
    Using AI Methods to Find a Non-Linear Regression Model with a Coupling Condition,
    Engineering Mechanics, Vol.17, (2011), No.5/6, pp.419-431, ISSN 1802-1484, Pavel Heriban
    journal article

    In the real-life engineering practice, non-linear regression models have to be designed rather often. To ensure their technical or physical feasibility, such models may, in addition, require another coupling condition. This paper describes two procedures for designing a specific non-linear model using AI methods. A Radial Basis Functions (RBF) based optimization is presented of the model using Genetic Algorithms (GA).
  • MATOUŠEK, R.:
    HC12: The Principle of CUDA Implementation,
    MENDEL 2010, pp.303-308, ISBN 978-80-214-4120-0, (2010), VUT
    conference paper
    akce: MENDEL 2010 - 16th International Conference on Soft Computing, Brno University of Technology, 23.06.2010-25.06.2010

    In this paper, we present new optimization algorithm denoted as HC12 which is extremely useful for massive parallel implementation. For the ground of the massive parallel implementation, the CUDA GPU technology was used.