1. ¹University of Craiova, Craiova, Romania
2. ²University of Dortmund, Dortmund, Germany
3. ³University of Westminster, London, UK
4. ⁴University of Cluj-Napoca, Cluj, Romania

Correspondence: R Stoean, University of Craiova, A.I. Cuza, No 13, 200585, Craiova, Romania. E-mail: ruxandra.stoean@inf.ucv.ro

Received September 2007; Accepted September 2008; Published online 19 November 2008.

Abstract

The paper presents a novel evolutionary technique constructed as an alternative of the standard support vector machines architecture. The approach adopts the learning strategy of the latter but aims to simplify and generalize its training, by offering a transparent substitute to the initial black-box. Contrary to the canonical technique, the evolutionary approach can at all times explicitly acquire the coefficients of the decision function, without any further constraints. Moreover, in order to converge, the evolutionary method does not require the positive (semi-)definition properties for kernels within nonlinear learning. Several potential structures, enhancements and additions are proposed, tested and confirmed using available benchmarking test problems. Computational results show the validity of the new approach in terms of runtime, prediction accuracy and flexibility.