IEEE (Hong Kong) Computational Intelligence Chapter
2005 FYP Competition:

Seminar in 2011:


IEEE Computational Intelligence Chapter (Hong Kong) successfully hosted a seminar on 6 July 2011. The topic was "Cultural-Based Particle Swarm Optimization for Multiobjective Optimization and Performance Metrics Ensemble" given by

Prof. Gary G. Yen, Oklahoma State University, USA.



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.


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Seminar in 2009:


IEEE Computational Intelligence Chapter (Hong Kong) successfully hosted a seminar on 26th June 2009. The topic was ' Non-Model Based Virtual Reality and Visualization Using Computer Vision' given by Prof. Jesse Jin, from The University of Newcastle, Australia.



Traditional computer generated virtual reality is model based one, ie, it is heavily relied on building 3D model of the scene. There are two major problems: the first one is building 3D models is very time consuming and resources greedy; the second one is that it would not give real scene, no matter how close it could be. This seminar introduces a new approach of 3D virtual reality by using computer vision techniques. It also introduces the work in augmented reality.


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Seminar in 2007:


IEEE Computational Intelligence Chapter (Hong Kong) successfully hosted a seminar on 28th March 2007. The topic was 'Fuzzy Neural Computational Intelligence' given by Prof. Zhenya He, from Southeast University, Nanjing, China.


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Seminar in 2004:


IEEE Computational Intelligence Chapter (Hong Kong) successfully hosted a seminar on 4th December 2004. The topic was 'Predicting the survival or failure of click-and-mortar corporations: A data mining approach' given by Dr. Indranil Bose, Associate Professor, from School of Business, The University of Hong Kong. The event was fruitful and beneficial to all participants.

Theme: With the boom in e-business, several corporations have emerged in the late nineties that have primarily conducted their business through the Internet and the Web. They have come to be known as the dotcoms or click-and-mortar corporations. The success of these companies has been short lived. This research is an investigation of the burst of the dotcom bubble from a financial perspective. Data from the financial statements of several survived and failed dotcom companies is used to compute financial ratios, which are analyzed using three data mining techniques - discriminant analysis, neural networks, and support vector machines to find out whether they can predict the financial fate of companies. Neural networks perform the task better than other techniques. Using discriminant analysis and neural networks, the key financial ratios that play a major role in the process of prediction are identified. Statistical tests are conducted to validate the findings.


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