Welcome to The 2nd International Conference On Innovations in Bio-inspired computing and Application 2011

Keynote Speeches



 


Professor Vaclav Snasel

 

Topic: Forgetting Curve and Ant Colony Optimization algorithm
Abstract:

The analysis of social networks is concentrated mainly on uncovering hidden relations and properties of network nodes (vertices). Most of the current approaches are focused mainly on different network types and network coefficients. On one hand, the analysis can be relatively simple and on the other hand more complex approaches to network dynamics can be used. In this lecture we introduce a novel social network analysis based on the so-called Forgetting Curve and Ant Colony Optimization (ACO) algorithm. We analyze a co-authorship network and identify two types of ties among its nodes. The Forgetting Curve and ACO are used to model the dynamics of such a network.
One of the most relevant features of social networks is the community structure. Since these networks are typically very complex, it is great interest to reduce these networks to much simpler. Clustering and low dimensional representation of high dimensional data are important problems in many diverse fields. In recent years various spectral methods to perform these tasks, based on the eigenvectors of adjacency matrices of graphs on the data have been developed. One of the successful models is based on theory of diffusion equation. It is closely related to Schrodingerˇ¦s Equation for a free particle. The diffusion equation is used for measure of diffusion distance. We apply diffusion distance for social network partitioning.

Biography:

Vaclav Snasel is Professor of Computer Science. He works as researcher and university teacher. He is Dean Faculty of Electrical Engineering and Computer Science.
Vaclav Snasel's research and development experience includes over 30 years in the Industry and Academia. He works in a multi-disciplinary environment involving artificial intelligence, social network, conceptual lattice, information retrieval, semantic web, knowledge management, data compression, machine intelligence, neural network, web intelligence, nature and Bio-inspired computing, data mining, and applied to various real world problems.
He has given more than 12 plenary lectures and conference tutorials in these areas. He has authored/co-authored several refereed journal/conference papers, books and book chapters. He has published more than 400 papers (176 papers are recorded at Web of Science).


Professor Han-Chieh Chao

 

Topic: The study for Utilizing Organic Computing to Construct a Ubiquitous Wireless Network
Abstract:

Organic Computing is a computing form with similar human organic biologically properties. It can be self-adaptive for any condition. As the rapid progress in information network, the people are surrounded by all kinds of information. Besides specific expert, common user is unable to fully understand this information has according to oneˇ¦s wishes. In order to solve this problem, we must develop a computation form for people's need which posses the elasticity and automatic operation, we call it "organic computing". Any system with these properties, we call it an "organic computing system". Hence, an "Organic Computing System" is a technical system, which adapts dynamically to the current conditions of its environment.
This research will induct the organic computing to Ubiquitous wireless network. We use the characteristic of organic computing to solve the problems of the Ubiquitous wireless network. For example, we use the self-organizationˇBself-configuration to solve the problem of device management of mobile Ad-hoc network architecture. We use the self-optimization to solve the problem of routing for the fast moving mobile terminals. We use the self-protection to solve the problem of link security. We use the self-healing to solve the network problem of the power limit.

Biography:

Han-Chieh Chao is a joint appointed Full Professor of the Department of Electronic Engineering and Institute of Computer Science & Information Engineering of National Ilan University, I-Lan, Taiwan, R.O.C. where he is also serving as the president. He was appointed as the Director of the Computer Center for Ministry of Education starting from September 2008 to July 2010. His research interests include High Speed Networks, Wireless Networks, IPv6 based Networks, Digital Creative Arts and Digital Divide. He received his MS and Ph.D. degrees in Electrical Engineering from Purdue University in 1989 and 1993 respectively. He has authored or co-authored 4 books and has published about 280 refereed professional research papers. He has completed 100 MSEE thesis students and 3 PhD students. Dr. Chao has received many research awards, including Purdue University SRC awards, and NSC research awards (National Science Council of Taiwan). He also received many funded research grants from NSC, Ministry of Education (MOE), RDEC, Industrial Technology of Research Institute, Institute of Information Industry and FarEasTone Telecommunications Lab. Dr. Chao is the Editor-in-Chief for IET Communications, Journal of Internet Technology, International Journal of Internet Protocol Technology and International Journal of Ad Hoc and Ubiquitous Computing. Dr. Chao has served as the guest editors for Mobile Networking and Applications (ACM MONET), IEEE JSAC, IEEE Communications Magazine, Computer Communications, IEE Proceedings Communications, the Computer Journal, Telecommunication Systems, Wireless Personal Communications, and Wireless Communications & Mobile Computing. Dr. Chao is an IEEE senior member and a Fellow of IET (IEE). He is a Chartered Fellow of British Computer Society.


Professor Gary G. Yen

 

Topic: CULTURAL-BASED PARTICLE SWARM OPTIMIZATION FOR MULTIOBJECTIVE OPTIMIZATION AND PERFORMANCE METRICS ENSEMBLE
Abstract:

Evolutionary computation is the study of biologically motivated computational paradigms which exert novel ideas and inspiration from natural evolution and adaptation. The applications of population-based heuristics in solving constrained and dynamic optimization problems have been receiving a growing interest from computational intelligence community. Most practical optimization problems are with the existence of constraints and uncertainties in which the fitness function changes through time and is subject to multiple constraints. In this study, we propose the cultural-based particle swarm optimization (PSO) to solve these problems with real-world complications. A cultural framework is introduced that incorporates the required information from the PSO into five sections of the belief space, namely situational knowledge, temporal knowledge, domain knowledge, normative knowledge, and spatial knowledge. The archived information is exploited to detect the changes in the environment and assists response to the change and constraints through a diversity based repulsion among particles and migration among swarms in the population space, also helps in selecting the leading particles in three different levels, personal, swarm, and global level. Comparison of the proposed cultural based PSO over numerous challenging constrained and dynamic benchmark problems demonstrates the competitive, if not appreciably much better, performance with respect to selected state-of-the-art PSO heuristics.
In addition, an ensemble method on performance metrics is proposed, knowing no single metric alone can faithfully quantify the performance of a given design under real-world scenarios. A collection of performance metrics, measuring the spread across the Pareto-optimal front and the ability to attain the global trade-off surface closeness, could be incorporated into the ensemble approach. This design allows a comprehensive measure and more importantly reveals additional insight pertaining to specific problem characteristics that the underlying MOEA could perform the best.

Biography:

Gary G. Yen received the Ph.D. degree in electrical and computer engineering from the University of Notre Dame, Notre Dame, Indiana in 1992. He is currently a Professor in the School of Electrical and Computer Engineering, Oklahoma State University (OSU). Before he joined OSU in 1997, he was with the Structure Control Division, U.S. Air Force Research Laboratory in Albuquerque, New Mexico. His research is supported by the DoD, DoE, EPA, NASA, NSF, and Process Industry. His research interest includes intelligent control, computational intelligence, evolutionary multiobjective optimization, conditional health monitoring, signal processing and their industrial/defense applications.
Gary was an associate editor of the IEEE Transactions on Neural Networks and IEEE Control Systems Magazine during 1994-1999, and of the IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man and Cybernetics and IFAC Journal on Automatica and Mechatronics. He is currently serving as an associate editor for the IEEE Transactions on Evolutionary Computation and International Journal of Swarm Intelligence Research. He served as the General Chair for the 2003 IEEE International Symposium on Intelligent Control held in Houston, TX and 2006 IEEE World Congress on Computational Intelligence held in Vancouver, Canada. Gary served as Vice President for the Technical Activities, IEEE Computational Intelligence Society in 2004-2005 and is the founding editor-in-chief of the IEEE Computational Intelligence Magazine from 2006 to 2009. He is currently serving as President of the IEEE Computational Intelligence Society in 2010-2011. He is a Fellow of IEEE.


 

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