An investigation into the use of genetic algorithms for shape recognition.
The use of the genetic algorithm for shape recognition has been investigated in relation to features
along a shape boundary contour. Various methods for encoding chromosomes were investigated, the
most successful of which led to the development of a new technique to input normalised
'perceptually important point' features from the contour into a genetic algorithm. Chromosomes
evolve with genes defining various ways of 'observing' different parts of the contour. The
normalisation process provides the capability for multi-scale spatial frequency filtering and
fine/coarse resolution of the contour features. A standard genetic algorithm was chosen for this
investigation because its performance can be analysed by applying schema analysis to the genes. A
new method for measurement of gene diversity has been developed. It is shown that this diversity
measure can be used to direct the genetic algorithm parameters to evolve a number of 'good'
chromosomes. In this way a variety of sections along the contour can be observed. A new and
effective recognition technique has been developed which makes use of these 'good' chromosomes
and the same fitness calculation as used in the genetic algorithm. Correct recognition can be achieved
by selecting chromosomes and adjusting two thresholds, the values of which are found not to be
critical. Difficulties associated with the calculation of a shape's fitness were analysed and the
structure of the genes in the chromosome investigated using schema and epistatic analysis. It was
shown that the behaviour of the genetic algorithm is compatible with the schema theorem of J. H.
Holland. Reasons are given to explain the minimum value for the mutation probability that is required
for the evolution of a number of' good' chromosomes. Suggestions for future research are made and,
in particular, it is recommended that the convergence properties of the standard genetic algorithm be