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 generalise 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.