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CIRG - Research  -  Evolutionary Computation 



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OVERVIEW

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.

ACTIVE MEMBERS

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ALUMNI MEMBERS

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

GROUP PUBLICATIONS

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MEMBER PROFILE



 Name:

 Gavin Potgieter

Portrait photo

 E-mail:

 engel@cs.up.ac.za

 Group(s):

 Evolutionary Computation
 Data Mining

 

 Degree specific information: M.Sc

 Title:

 Mining continuous classes using evolutionary computing.

 Abstract:

Data mining is the term given to knowledge discovery paradigms that attempt to infer knowledge, in the form of rules, from structured data using machine learning algorithms. Specifically, data mining attempts to infer rules that are accurate, crisp, comprehensible and interesting. There are not many data mining algorithms for mining continuous classes. This thesis develops a new approach for mining continuous classes. The approach is based on a genetic program, which utilises an efficient genetic algorithm approach to evolve the non-linear regressions described by the leaf nodes of individuals in the genetic program's population. The approach also optimises the learning process by using an efficient, fast data clustering algorithm to reduce the training pattern search space. Experimental results from both algorithms are compared with results obtained from a neural network. The experimental results of the genetic program is also compared against a commercial data mining package (Cubist). These results indicate that the genetic algorithm technique is substantially faster than the neural network, and produces comparable accuracy. The genetic program produces substantially less complex rules than that of both the neural network and Cubist.

 Supervisor / Co-Supervisor:

 AP Engelbrecht

 Thesis:

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