OpenAIR @ RGU >
Design and Technology >
Theses (Engineering) >
Please use this identifier to cite or link to this item:
|Title: ||The synthesis of artificial neural networks using single string evolutionary techniques.|
|Authors: ||MacLeod, Christopher|
|Supervisors: ||Maxwell, Grant M.|
|Keywords: ||Artificial neural networks|
|Issue Date: ||1999|
|Publisher: ||The Robert Gordon University|
|Abstract: ||The research presented in this thesis is concerned with optimising the structure of
Artificial Neural Networks. These techniques are based on computer modelling of
biological evolution or foetal development. They are known as Evolutionary, Genetic
or Embryological methods. Specifically, Embryological techniques are used to grow
Artificial Neural Network topologies. The Embryological Algorithm is an alternative
to the popular Genetic Algorithm, which is widely used to achieve similar results.
The algorithm grows in the sense that the network structure is added to incrementally
and thus changes from a simple form to a more complex form. This is unlike the
Genetic Algorithm, which causes the structure of the network to evolve in an
unstructured or random way.
The thesis outlines the following original work: The operation of the Embryological
Algorithm is described and compared with the Genetic Algorithm. The results of an
exhaustive literature search in the subject area are reported. The growth strategies
which may be used to evolve Artificial Neural Network structure are listed. These
growth strategies are integrated into an algorithm for network growth. Experimental
results obtained from using such a system are described and there is a discussion of
the applications of the approach. Consideration is given of the advantages and
disadvantages of this technique and suggestions are made for future work in the area.
A new learning algorithm based on Taguchi methods is also described.
The report concludes that the method of incremental growth is a useful and powerful
technique for defining neural network structures and is more efficient than its
alternatives. Recommendations are also made with regard to the types of network to
which this approach is best suited.
Finally, the report contains a discussion of two important aspects of Genetic or
Evolutionary techniques related to the above. These are Modular networks (and their
synthesis) and the functionality of the network itself.|
|Appears in Collections:||Theses (Engineering)|
All items in OpenAIR are protected by copyright, with all rights reserved.