  The present paper deals with optimisation of Nearest Neighbour rule
Classifiers via Genetic Algorithms. The methodology consists on implement a
Genetic Algorithm capable of search the input feature space used by the NNR
classifier. Results show that is adequate to perform feature reduction and
simultaneous improve the Recognition Rate. Some practical examples prove that
is possible to Recognise Portuguese Granites in 100%, with only 3 morphological
features (from an original set of 117 features), which is well suited for real
time applications. Moreover, the present method represents a robust strategy to
understand the proper nature of the images treated, and their discriminant
features. KEYWORDS: Feature Reduction, Genetic Algorithms, Nearest Neighbour
Rule Classifiers (k-NNR).
