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CURRENT MEMBERS

Below is a list of the doctorate members actively conducting research at CIRG. Click on each name for more detailed information on the researcher and his/her project.


M Snyman

PhD Started in 2009

P Raharja

PhD Started in 2009

M Mtshali

PhD Started in 2009

B Kalema

PhD Started in 2009

J Grobler

PhD Started in 2009
M.Eng Completed in 2009

M Ahmad

PhD Started in 2009

T Museba

PhD Started in 2008

L Li

PhD Started in 2008

C Naicker

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

K Malan

PhD Started in 2007

M Greeff

PhD Started in 2007

B Baridam

PhD Started in 2007

MC du Plessis

PhD Started in 2006

D Constantinou

PhD Started in 2006

A Graaff

PhD Started in 2005
M.Sc Completed in 2003

N Franken

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

A Ismail

PhD Started in 2005
M.Sc Completed in 2001

ALUMNI

Below is a list of previous doctorate members that conducted research at CIRG. Click on each name for more detailed information on the researcher and his/her project.


L Schoeman

PhD Completed in 2010

P Lutu

PhD Completed in 2010

S Khan

PhD Completed in 2009

D Rodic

PhD Completed in 2005
M.Sc Completed in 1999

M Omran

PhD Completed in 2005

F van den Bergh

PhD Completed in 2002

MEMBER PROFILE



 Name:

 Nelis Franken

Portrait photo

 E-mail:

 nfranken@cs.up.ac.za

 Group(s):

 Evolutionary Computation
 Swarm Intelligence
 Games

 

 Degree specific information: PhD

 Title:

 Variable Length Particles for PSO

 Abstract:

Not available

 Supervisor / Co-Supervisor:

 AP Engelbrecht

 Thesis:

 Not available for download yet.

 

 Degree specific information: M.Sc

 Title:

 PSO-Based Coevolutionary Game Learning

 Abstract:

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

 Thesis:

 Download




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