Evolutionary support vector machines represent a new learning
technique that we recently developed as a hybridization between
support vector machines and evolutionary algorithms, regarding the
discovery of the optimal decision function within the former. The
new approach has proven to be successful as binary classification
problems have been concerned. Present paper presents the extension
of the aforementioned technique to the more frequent case of
multi-class classification. Validation of evolutionary multi-class
support vector machines is performed on the well-known benchmark
problem of Fisher's Iris plants classification and results
demonstrate the promise of the new approach.