Keynote Speaker


Prof. Laszlo T. Koczy


Laszlo T. Koczy received the M.Sc., M.Phil. and Ph.D. degrees from the Technical University of Budapest (BME) in 1975, 1976 and 1977, respectively; and the D.Sc. degree from the Hungarian Academy of Science in 1998. He spent his career at BME until 2001, and from 2002 at Szechenyi Istvan University (Gyor, SZE). He has been from 2002 to 2011 Dean of Engineering, and from 2013 to current President of the University Research Council and of the University Ph.D. Council. From 2012 he has been a member of the Hungarian Accreditation Committee (for higher education), appointed by the Prime Minister, and elected Chair of the Engineering and Computer Science sub-committee, member of the Professors and Ph.D. sub-committee, and has been a member of the National Doctoral Council since 2012. Besides, he becomes a member of the St. Stephan Academy of Sciences since 2016.

He has been a visiting professor in Australia (ANU, UNSW, Murdoch and Deakin), in Japan (TIT, being LIFE Endowed Fuzzy Theory Chair Professor), in Korea (POSTECH), Austria (J. Kepler U.), and Italy (U. of Trento), etc.

His research interests are fuzzy systems, evolutionary algorithms and neural networks as well as applications. He has published over 580 articles, most of those being refereed papers, and several text books on the subject. He has close to 2600 independent citations ( and his Hirsch-index is 34 by Google Scholar (based on ~4500 citations there).

His main results are: he did introduce the concept of rule interpolation in sparse fuzzy models, and hierarchical interpolative fuzzy systems, fuzzy Hough transform, and also fuzzy signatures and fuzzy situational maps, further fuzzy signature state machines among others. His research interests include applications of CI for telecommunication, transportation and logistics, vehicles and mobile robots, control, information retrieval, etc.

He was an Associate Editor of IEEE TFS for several periods, and is now AE of Fuzzy Sets and Systems, Int. Journal of Fuzzy Systems, Journal of Advanced Computational Intelligence, Int. J. of Fuzzy Systems, Soft Computing, etc. He is a Fellow of IFSA, of ISME and of the Hungarian Academy of Engineering. He was the founding President and is now the Life Honorary President of the Hungarian Fuzzy Association, was President, etc. of IFSA, AdCom member of IEEE CIS, and of IEEE Systems Council, etc.

Recently, he won the Best Paper Award at the International Fuzzy Systems Association World Congress 2017 in Otsu, Japan, and received the Commander Cross of the Order of Hungary for his professorial work from the President of the country in 2017.


Optimization by Evolutionary and Memetic Algorithms

In recent years a large number of evolutionary and other population based heuristics were proposed in the literature for solving NP-hard optimization problems. Such optimization tasks are mathematically intractable, but several approximation approaches do exist. Exact optimization usually works only to very limited size instances, but with slight sub-optimality the problem size might be considerably extended.

Evolutionary algorithms mimic the process of evolution in the nature, mammals or single cell organizations, or even the social behavior of groups of animals. Such algorithms are global searchers, and thus they do not get stuck in local optima. However, their convergence speed is not very high. Because of this, Moscato proposed a combined approach: local search nested into the global search cycle. This hybrid approach is called memetic algorithm.

In the past decades, the speaker and his group devoted their attention to the comparison and analysis of various evolutionary and memetic approaches, often in combination with fuzzy rule based models where parameters had to be optimized in order to obtain an optimal approximation model.

Even though in the literature still the classic Genetic Algorithm dominates, our investigations clearly showed that the Bacterial Evolutionary Algorithm (BEA) proposed by Furuhashi and group is far more efficient. The combination of BEA with the sophisticated Levenberg-Marquard local search produced excellent result on a series of benchmark problems. Comparison of various approaches on various size instances will be presented.

As an entirely novel approach, we also investigated the combination of BEA with discrete local search on classic NP-hard problems, such as the Travelling Salesman Problem (TSP), and the Bin Packing Problem (BPP).

In 2015 we presented a Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) for the Traveling Salesman Problem. It provided results tested on a series of TSP problems (taken from logistics and VLSI design), the benchmarks going up to 5000 or 10000 nodes. The results surpassed almost all state of the art approaches (Lin-Kernighan, Concorde) and turned out to be inferior only to Helsgaun’s algorithm.