Soft Computing - GEFS 2010: Plenary Lecturers

Lecturers

There will be three plenary lecturers in this fourth edition of the GEFS workshop:

Dr. Francisco Herrera
Dr. Frank Hoffmann
Dr. Eyke Hüllermeier

Francisco Herrera

Francisco Herrera received the M.Sc. degree in Mathematics in 1988 and the Ph.D. degree in Mathematics in 1991, both from the University of Granada, Spain. He is currently a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada. He has published more than 150 papers in international journals. He is coauthor of the book “Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases" (World Scientific, 2001). As edited activities, he has co-edited five international books and co-edited twenty special issues in international journals on different Soft Computing topics. He acts as associated editor of the journals: IEEE Transactions on Fuzzy Systesms, Mathware and Soft Computing, Advances in Fuzzy Systems, Advances in Computational Sciences and Technology, and International Journal of Applied Metaheuristic Computing. He currently serves as area editor of the Journal Soft Computing (area of genetic algorithms and genetic fuzzy systems), and he serves as member of the editorial board of the journals: Fuzzy Sets and Systems, Applied Intelligence, Knowledge and Information Systems, Information Fusion, Evolutionary Intelligence, International Journal of Hybrid Intelligent Systems, Memetic Computation, International Journal of Computational Intelligence Research, The Open Cybernetics and Systemics Journal, Recent Patents on Computer Science, Journal of Advanced Research in Fuzzy and Uncertain Systems, International Journal of Information Technology and Intelligent and Computing, and Journal of Artificial Intelligence and Soft Computing Research.

His current research interests include computing with words and decision making, data mining, data preparation, instance selection, fuzzy rule based systems, genetic fuzzy systems, knowledge extraction based on evolutionary algorithms, memetic algorithms and genetic algorithms.

Lecture title: Genetic Fuzzy Systems for Subgroup Discovery. Models and Applications

The problem of subgroup discovery which can be defined as: Given a population of individuals and a property of those individuals, we are interested in finding a population of subgroups as large as possible and in having the most unusual statistical characteristic with respect to the property of interest. Subgroup discovery is well suited for finding such dependencies, i.e., discovering relations between a dependent variable (target variable) and (several) independent variables. The discovered subgroup patterns must essentially satisfy two conditions. First, they have to be interpretable for the analyst, and second they need to be interesting according to the criteria of the user. The discovery of (interesting) subgroups has a high practical relevance in all domains of science or business. This talk presents the use of genetic algorithms for extracting fuzzy rules for subgroup discovery, discussing advantages, challenges and applications.

Frank Hoffmann

Frank Hoffmann received the Ph.D. degree in physics from the University of Kiel, Germany in 1996. From 1996 to 2000 He has been with University of California at Berkeley as a visiting researcher. He was lecturer in computer science at the Royal Institute of Technology in Stockholm, Sweden from 2000-2002. Since 2003 he is a senior researcher at the Technische Universität Dortmund. He served as general chair of WSC8 and GEFS´08. He is associate editor of IEEE Systems, Man and Cybernetics Part B and Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. His research interests are in the area of computational intelligence, robotics and computer vision.

Lecture title: Trends and challenges in evolutionary design and optimization

The straightforward application of evolutionary algorithms to real word problems, and in particular evolutionary design of complex systems, is severly handicapped by vagueness and uncertainty in the formulations of objectives and constraints, time- or cost-consuming evaluation of solutions, interconnection of multiple sub-systems, properties on multiple diverse system levels and non-stationary environments. This talk outlines recent trends and challenges in evolutionary optimization such as multiobjective structural optimization, vagueness in objective formulation, surrogate fitness models, efficient global optimization, scalability, non-stationary fitness functions, robustness, development and evolution and life-like design. It discusses the approaches and achievements in those areas in the recent past and illustrate the substantial challenges and promising directions of research that lie ahead.

Eyke Hüllermeier

Eyke Hüllermeier is with the Department of Mathematics and Computer Science at Marburg University (Germany), where he holds an appointment as a full professor and heads the Knowledge Engineering and Bioinformatics Lab. He holds M.Sc. degrees in mathematics and business computing, a Ph.D. in computer science, and a Habilitation degree, all from the University of Paderborn (Germany). His research interests are focused on machine learning and data mining, fuzzy set theory, uncertainty and approximate reasoning, and applications in bioinformatics. He has published more than 100 research papers on these topics in peer-reviewed journals and major international conferences. He is a Member of the IEEE, the IEEE Computational Intelligence Society, and a board member of the European Society for Fuzzy Logic and Technology (EUSFLAT). He is on the editorial board of several journals, including Fuzzy Sets and Systems and Soft Computing. Moreover, he is a coordinator of the EUSFLAT working group on Learning and Data Mining, and the head of the IEEE CIS Task Force on Machine Learning.

Lecture title: Fuzzy Rule Induction and Related Problems. A Case for Genetic Search

Rule induction for classification has a long tradition is the field of machine learning and, despite having widely reached a state of maturity, still enjoys great popularity. In fact, the interest in learning rule-based models, mostly for the purpose of prediction, goes far beyond the field of machine learning itself and also includes other research areas, notably fuzzy systems and computational intelligence. Interestingly, quite different methodologies for rule learning have been established in machine learning on the one side and in computational intelligence on the other side: Whereas state-of-the-art rule learner in machine learning are almost exclusively driven by heuristic search methods, mostly of a greedy nature, fuzzy rule models are commonly optimized by means of genetic and evolutionary search techniques. The purpose of this talk is to compare these two methodologies and to throw a critical glance at genetic fuzzy systems from the point of view of machine learning. Beyond that, another purpose of the talk is to highlight the potential of fuzzy set-based extensions in data-driven model construction, not only for rule induction but also for related learning problems that have recently attracted interest in the research community.

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