The basis of this thesis is the presumption that while neural networks are useful structures that can be used to model complex, highly non-linear systems, current methods of training the neural networks are inadequate in some problem domains. Genetic algorithms have been used to optimise both the weights and architectures of neural networks, but these approaches do not treat the neural network in a sensible manner. In this thesis, I define the basis of computation within a neural network as a single neuron and its associated input connections. Sets of these neurons, stored in a matrix representation, comprise the building blocks that are transferred during one or more epochs of a genetic algorithm. I develop the concept of a Neural Building Block and two new genetic algorithms are created that utilise this concept. The first genetic algorithm utilises the micro neural building block (micro-NBB); a unit consisting of one or more neurons and their input connections. The micro-NBB is a unit that is transmitted through the process of crossover and hence requires the introduction of a new crossover operator. However the micro NBB can not be stored as a reusable component and must exist only as the product of the crossover operator. The macro neural building block (macro-NBB) is utilised in the second genetic algorithm, and encapsulates the idea that fit neural networks contain fit sub-networks, that need to be preserved across multiple epochs. A macro-NBB is a micro-NBB that exists across multiple epochs. Macro-NBBs must exist across multiple epochs, and this necessitates the use of a genetic store, and a new operator to introduce macro-NBBs back into the population at random intervals. Once the theoretical presentation is completed the newly developed genetic algorithms are used to evolve weights for a variety of architectures of neural networks to demonstrate the feasibility of the approach. Comparison of the new genetic algorithm with other approaches is very favourable on two problems: a multiplexer problem and a robot control problem.
| Identifer | oai:union.ndltd.org:ADTP/216889 |
| Date | January 2000 |
| Creators | Salama, Rameri |
| Publisher | University of Western Australia. Dept. of Computer Science |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | Copyright Rameri Salama, http://www.itpo.uwa.edu.au/UWA-Computer-And-Software-Use-Regulations.html |
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