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The Swarm Intelligence focus area is currently the most active in the group, with the largest number of members. The focus area's main interest is particle swarm optimization (PSO), with the development of new and improved PSO algorithms. Theoretical analyses of PSO are also being done, with convergence proofs being studied. Techniques are developed for constrained optimization, niching (locating multiple solutions), multi-objective optimization, dynamic optimization problems, and to cope with discrete search spaces.

Applications of PSO techniques that are under investigation include the coevolutionary training of neural networks for game playing and financial traders, scheduling, image analysis, and data clustering. The research focus area is also investigating the application of ant colony optimization techniques to exploratory data analysis, workload distribution in computer grids, energy efficient routing in mobile ad hoc networks, and network topology design.


List the current members actively doing research in this focus area. [ Show ]


L Schoeman

PhD Completed in 2010

J Grobler

PhD Started in 2009
M.Eng Completed in 2009

S Khan

PhD Completed in 2009

D Barla-Szabo

M.Sc Started in 2003
Hons-B.Sc Completed in 2002

M Neethling

M.Sc Completed in 2008

F Zablocki

M.Sc Completed in 2008

E Papacostantis

M.Sc Started in 2004
Hons-B.Sc Completed in 2003

L Messerschmidt

M.Sc Completed

C Naicker

PhD Started in 2007
M.Sc Completed in 2006
Hons-B.Sc Completed in 2002

J Conradie

Hons-B.Sc Completed in 2004

J du Plessis

M.Sc Completed in 2005

E Peer

M.Sc Completed in 2005

M Omran

PhD Completed in 2005

G Pampara

M.Sc Started in 2005
Hons-B.Sc Completed in 2004

E van Loggerenberg

M.Sc Started in 2005
Hons-B.Sc Completed in 2004

N Franken

PhD Started in 2005
M.Sc Completed in 2004
Hons-B.Sc Completed in 2002

R Brits

M.Sc Completed in 2003
Hons-B.Sc Completed in 2000

U Paquet

M.Sc Completed in 2003

F van den Bergh

PhD Completed in 2002

A Ismail

PhD Started in 2005
M.Sc Completed in 2001


List publications of this research focus area. [ Show ]



 Evangelos Papacostantis

Portrait photo




 Swarm Intelligence
 Evolutionary Computation


 Degree specific information: M.Sc


 Co-Evolutionary Approach to Probabilistic Game Learning using PSO


The domain of complex board games has been under the microscope for many years in the field of AI. These games provide an ideal testing ground to explore a number of AI techniques, concepts and approaches. Games that are more representative of real world problems are probabilistic games or otherwise called non-deterministic games. These games have imperfect information, meaning that a players' actions within the game is determined by random/probabilistic elements. Co-evolution techniques have successfully been applied to these games, allowing competitive and reasonably intelligent agents to immerge. Co-evolution techniques allow unintelligent/random agents to compete against each other and incrementally learn from better performing agents. This research will generally investigate how co-evolution and PSO techniques can be used to find competitive probabilistic game playing agents. Different PSO topologies are going to be examined together with different co-evolution schemes. Backgammon and Poker are the two probabilistic games which are going to be used for evaluation purposes.

 Supervisor / Co-Supervisor:

 AP Engelbrecht
 N Franken


 Not available for download yet.


 Degree specific information: Hons-B.Sc


 Development of advanced data analysis/data mining tool


The cerebral cortex is arguably the most fascinating structure in all of human physiology. The ability to associate items according to their similarities/differences was first pointed out by Aristotle. This project includes the creation of an advanced data analysis/data mining tool, by attempting to simulate the cerebral cortex. It will employ self-organizing feature maps, that compress high dimensional data into two dimensional maps, accomplished by different unsupervised training algorithms. It will allow features such as association, classification, path finding and forecasting. A number of visual techniques will be implemented on the two dimensional maps to reveal all these features. The tool will be further more improved by adding knowledge exploration features with the use of decision trees, rule inductions and genetic algorithms.

 Supervisor / Co-Supervisor:

 AP Engelbrecht


 Not available for download yet.

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