Title page for ETD etd-06092005-091517


Document Type Master's Dissertation
Author Nel, Gert M
Email gmn@ucs.co.za
URN etd-06092005-091517
Document Title A memetic genetic program for knowledge discovery
Degree MSc (Computer Science)
Department Computer Science
Supervisor
Advisor Name Title
Prof A P Engelbrecht Committee Chair
Keywords
  • global search
  • classification problems
  • optimisation
  • local search
  • genetic program
  • decision trees
  • BGP
  • MBGP.
  • building block hypothesis
  • memetic algorithms
  • evolutionary algorithms
Date 2005-01-04
Availability unrestricted
Abstract
Local search algorithms have been proved to be effective in refining solutions that have been found by other algorithms. Evolutionary algorithms, in particular global search algorithms, have shown to be successful in producing approximate solutions for optimisation and classification problems in acceptable computation times. A relatively new method, memetic algorithms, uses local search to refine the approximate solutions produced by global search algorithms. This thesis develops such a memetic algorithm. The global search algorithm used as part of the new memetic algorithm is a genetic program that implements the building block hypothesis by building simplistic decision trees representing valid solutions, and gradually increases the complexity of the trees. The specific building block hypothesis implementation is known as the building block approach to genetic programming, BGP. The effectiveness and efficiency of the new memetic algorithm, which combines the BGP algorithm with a local search algorithm, is demonstrated.
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