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CURRENT MEMBERS

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


A van Wyk

M.Sc Started in 2009
Hons-B.Sc Completed in 2008

S van Eeden

M.Sc Started in 2009

B Anguelov

M.Sc Started in 2009
Hons-B.Sc Completed in 2008

PG Ferreira

M.Sc Started in 2009

T Scheepers

M.Sc Started in 2008

M Riekert

M.Sc Started in 2008
Hons-B.Sc Completed in 2007

J Nicholls

M.Sc Started in 2008

T Naidoo

M.Sc Started in 2008

W Matthysen

M.Sc Started in 2008
Hons-B.Sc Completed in 2007

L Langenhoven

M.Sc Started in 2008
Hons-B.Sc Completed in 2006

M Da Silva

M.Sc Started in 2008

R Vlietstra

M.Sc Started in 2007

M van der Merwe

M.Sc Started in 2007

M Smit

M.Sc Started in 2007

A Rakitianskaia

M.Sc Started in 2007

J Duhain

M.Sc Started in 2007

A Louis

M.Sc Started in 2006

R Klazar

M.Sc Started in 2006

T Cloete

M.Sc Started in 2006

A Hauptfleisch

M.Sc Started in 2006

R Brink

M.Sc Started in 2006

S Allen

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

G Pampara

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

A Edwards

M.Sc Started in 2005

A Brenner

M.Sc Started in 2005

E Papacostantis

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

D Barla-Szabo

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

E Dean

M.Sc Started in 2002

W van Heerden

M.Sc Started in 2002

E Basson

M.Sc Started in 1999
Hons-B.Sc Completed in 1998

ALUMNI

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


M Poggiolini

M.Sc Completed in 2009

F Zablocki

M.Sc Completed in 2008

J Pun

M.Sc Completed

C Naicker

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

H Grobler

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

L Messerschmidt

M.Sc Completed

M Neethling

M.Sc Completed in 2008

W Duminy

M.Sc Completed in 2007

J du Plessis

M.Sc Completed in 2005

E Peer

M.Sc Completed in 2005

G Nel

M.Sc Completed in 2005

N Franken

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

A Graaff

PhD Started in 2005
M.Sc Completed in 2003

R Brits

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

U Paquet

M.Sc Completed in 2003

G Potgieter

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

D van Wyk

M.IT Completed in 2003

A Ismail

PhD Started in 2005
M.Sc Completed in 2001

D Rodic

PhD Completed in 2005
M.Sc Completed in 1999

A Adejumo

M.Sc Completed in 1999

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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