الفهرس 
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Abstract
Artificial neural networks exhibit many useful and unique attributes. Hopfield neural
networks are very good at recognizing patterns in noisy data. The variations of the techniques
for training Hopfield neural networks are computationally expensive, and not always able to
find a good solution. In short, Hopfield neural networks can be difficult and expensive to
train.
Evolutionary methods such as genetic programming (GP) are global search techniques based
on the evolution of a given population. In this population every individual represents a
solution for a problem that is intended to be solved. The evolution is achieved by means of
selection of the best individuals although the worst ones also have a little chance of being
selected. This process is developed using selection, crossover and mutation operations. After
several generations, it is expected that the population might contains some good solutions for
the problem. The GP encodes for the solutions is tree-shaped, so the user must specify which
terminals (leaves of the tree) and functions (nodes with children) will be used by the
evolutionary algorithm in order to build complex expressions.
Pattern recognition is an important practical application of associative memory. The task is
to produce a clear, noise-free pattern at the output when the input vectors are noisy versions of
the trained patterns
This thesis investigates the benefits of combining Hopfield neural networks and genetic
programming into hybrid system for classification systems, especially for applications
character recognition tasks. The research reported in this thesis investigates the possibility of
using an evolutionary approach to improve Hopfield neural networks.
The technique investigated by this thesis is the use of genetic programming to evolve the
structure of the Hopfield neural network. The structure of the gene used by this technique
obviates the need for real values to be encoded on the chromosome. This thesis tests the
technique on real life problems. The proposed method is applied on two different data sets
character recognition and spoken Arabic digits.