Support vector machines are a modern and very efficient learning
heuristic. However, their internal engine relies on not very easy
or common mathematical concepts. The paper presents a newly
developed simpler design of the engine, built through the means of
evolutionary computation, in the context of nonlinear support
vector machines. Experiments are carried on fictitious
2-dimensional points data sets and demonstrate once again the
promise of the new approach.