Present paper addresses the famous machine learning paradigm, called support
vector machines, from the viewpoint of evolutionary computation. Namely, the constrained
optimization problem within support vector machines is solved through an evolutionary algorithm,
for the sake of simplicity. The new approach has been so far applied solely to the
case of linear support vector machines for separable data. Experiments are conducted on a
fictitious 2-dimensional points data set and are very promising.