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The focus of this research area is on the development of evolutionary algorithms, specifically new differential evolution algorithms, with application to dynamic environments, and coevolution. Competitive coevolution strategies in combination with particle swarm optimizers are investigated for training strategists from zero knowledge.

Applications are to solve optimization problems, game playing, data mining, and financial trading.


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


S Khan

PhD Completed in 2009

W Duminy

M.Sc Completed in 2007

L Messerschmidt

M.Sc Completed

G Nel

M.Sc Completed in 2005

N Franken

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

G Potgieter

M.Sc Completed in 2003
Hons-B.Sc Completed in 2001

D van Wyk

M.IT Completed in 2003


List publications of this research focus area. [ Show ]



 Nelis Franken

Portrait photo




 Evolutionary Computation
 Swarm Intelligence


 Degree specific information: PhD


 Variable Length Particles for PSO


Not available

 Supervisor / Co-Supervisor:

 AP Engelbrecht


 Not available for download yet.


 Degree specific information: M.Sc


 PSO-Based Coevolutionary Game Learning


Games have been investigated as computationally complex problems since the inception of artificial intelligence in the 1950's. Originally, search-based techniques were applied to create a competent (and sometimes even expert) game player. The search-based techniques, such as game trees, made use of human-defined knowledge to evaluate the current game state and recommend the best move to make next. Recent research has shown that neural networks can be evolved as game state evaluators, thereby removing the human intelligence factor completely. This study builds on the initial research that made use of evolutionary programming to evolve neural networks in the game learning domain. Particle Swarm Optimisation (PSO) is applied inside a coevolutionary training environment to evolve the weights of the neural network. The training technique is applied to both the zero sum and non-zero sum game domains, with specific application to Tic-Tac-Toe, Checkers and the Iterated Prisoner's Dilemma (IPD). The influence of the various PSO parameters on playing performance are experimentally examined, and the overall performance of three different neighbourhood information sharing structures compared. A new coevolutionary scoring scheme and particle dispersement operator are defined, inspired by Formula One Grand Prix racing. Finally, the PSO is applied in three novel ways to evolve strategies for the IPD -- the first application of its kind in the PSO field. The PSO-based coevolutionary learning technique described and examined in this study shows promise in evolving intelligent evaluators for the aforementioned games, and further study will be conducted to analyse its scalability to larger search spaces and games of varying complexity.

 Supervisor / Co-Supervisor:

 AP Engelbrecht



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