Plenary Speakers

 Mario Köpen
KYUSHU INSTITUTE OF TECHNOLOGY, Japan 
  Ponnuthurai Nagaratnam Suganthan
NTU, Singapore  
   

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Gestalt Aspects of Security Patterns
Mario Köpen
KYUSHU INSTITUTE OF TECHNOLOGY, Japan  


 [Abstract] Gestalt laws of vision are an apparent phenomenon of visual perception that still lack general understanding, despite more than 100 years after its first mentioning in psychological literature have passed. In this contribution, we want to promote Gestalt as challenge to computer science. We want to focus on the observation that Gestalt laws strongly affect the way, in which we organize and structure our living environment, and thus adapting to the needs, possibilities, but also limitations of human cognition. This also refers to forensic case studies, and the ambiguous role of Gestalt there: Gestalt can help to discover and gain evidence from visual examination, but it can also simply cause the overlooking of essential visual cues. An experimental study on information hiding by the method of modifying a logo image will demonstrate that attention for changes is not a linear function of the amount of change. In this sense, a number of (probably even simple) forensic techniques and "rules of thumb" can be also seen as employing Gestalt laws for revealing forensic evidence.

This gives some understanding for an interest in the computational handling of Gestalt. In a second part, the state of research on Gestalt in engineering sciences, esp. image processing and pattern analysis, will be critically reviewed, and their strong and weak points will be evaluated. But moreover, new emerging computational paradigms and models will be evaluated according to what they might provide for the understanding of Gestalt. Among these paradigms and models, we can find the Neural Darwinism, which relates evolutionary concepts to the processing of the brain, the recently proposed Cogency Confabulation, which relates learning with the maximization of a priori probability, and Jeff Hawkins hierarchical temporal memory model of the brain that comes most close to a brain processing model including Gestalt laws "for free," while maintaining a computational model at the same time.


 [Biography]

Mario Köpen was born in 1964. He studied physics at the Humboldt-University of Berlin and received his master degree in solid state physics in 1991. Afterwards, he worked as scientific assistant at the Central Institute for Cybernetics and Information Processing in Berlin and changed his main research interests to image processing and neural networks. From 1992 to 2006, he was working with the Fraunhofer Institute for Production Systems and Design Technology. He continued his works on the industrial applications of image processing, pattern recognition, and soft computing, esp. evolutionary computation. During this period, he achieved the doctoral degree at the Technical University Berlin with his thesis works: "Development of an intelligent image processing system by using soft computing" with honors. He has published around 100 peer-reviewed papers in conference proceedings, journals and books and was active in the organization of various conferences as chair or member of the program committee, incl. the WSC on-line conference series on Soft Computing in Industrial Applications, and the HIS conference series on Hybrid Intelligent Systems. He is founding member of the World Federation of Soft Computing, and also member of the editorial board of the Applied Soft Computing journal, the Intl. Journal on Hybrid Intelligent Systems and the Intl. Journal on Computational Intelligence Research. In 2006, he became JSPS fellow at the Kyushu Institute of Technology in Japan, and in 2008 Professor at the Network Design and Reserach Center (NDRC) of the Kyushu Institute of Technology, where he is conducting now research in the fields of multi-objective optimization, digital convergence and multimodal content management.

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Ensemble Methods For Evolutionary Algorithms
Ponnuthurai Nagaratnam Suganthan
NTU, Singapore  


 [Abstract]Over the last 4-5 decades, evolutionary computation researchers have proposed several alternative approaches to construct evolutionary algorithms (EAs). Some such alternatives are one-point / two-points / uniform crossover operators, tournament / ranking / stochastic uniform sampling selection methods, clearing / crowding / sharing based niching algorithms, adaptive penalty / epsilon / superiority of feasible constraint handling approaches and so on. Clearly, there are several alternative approaches at every step of an EA and users will have to perform numerous simulations to pick the best approaches. In addition, each approach may require users to fine tune associated parameters. Furthermore, at different stages of evolution, different operators and different parameter values may be more appropriate. Therefore, the trial and error approach to operator selection and associated parameter tuning is not efficient. Recently, we proposed an ensemble strategy to benefit from both the availability of diverse operators and wide range of the associated parameters. Our research has shown the general applicability of the ensemble strategy in solving diverse problems by using different populated optimization algorithms. This talk will present some of our resent results along this novel research direction.

 [Biography]

Ponnuthurai Nagaratnam Suganthan received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990, 1992 and 1994, respectively. He obtained his Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He was a predoctoral Research Assistant in the Dept of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Dept of Computer Science and Electrical Engineering, University of Queensland in 1996–99. Since 1999 he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore where he was an Assistant Professor and now is an Associate Professor. He is an Editorial Board Member of the Evolutionary Computation Journal, MIT Press. He is an associate editor of the IEEE Trans on Evolutionary Computation, Information Sciences (Elsevier), Pattern Recognition (Elsevier) and Int. J. of Swarm Intelligence Research Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary Computation, an Elsevier Journal. His co-authored SaDE (April 2009) paper won "IEEE Trans. on Evolutionary Computation" outstanding paper award in 2012. His research interests include evolutionary computation, pattern recognition, multi-objective evolutionary algorithms, bioinformatics, applications of evolutionary computation and neural networks. His publications have been well cited (Googlescholar Citations). He is a Senior Member of the IEEE.