CIRG - Research  -  Evolutionary Computation 

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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 ]



 Willem Duminy

Portrait photo




 Evolutionary Computation


 Degree specific information: M.Sc


 An evolutionary approach to learn the components of evaluation functions


The three topics: evolutionary computing, machine learning and perfect information games combine in an experiment that investigates a method to increase the skill of a game-playing agent from zero game knowledge. The research entails the definition of a learning framework that makes use of genetic- and evolutionary- techniques to improve the performance two-person perfect-information game agents. Initially, the agent is equipped with the game rules and the ability to "percieve" elementary properties of a game state. A example of such a property might be "how many of my kings are on row four of the board". The properties are combined using a set of primitive operations to form features that are used in the evaluation function. The work of the learning agent is to discover these features and then determine the weights assigned to the features for optimal playing performance. The idea is to start with no game knowledge and through repeated application of the discovery step and weight optimisation step, the agent will gradually improve. The features and the weights can be seen as two genes that adapt and influence each other during the learning process. Individuals with strong features cooperate in a co-evolutionary fashion with those with strong weights. Competitive co-evolution ensures the improvement of a single gene: playing agents against each other to improve weights; or eliminating weaker feature sets.

 Supervisor / Co-Supervisor:

 AP Engelbrecht


 Not available for download yet.

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