Support vector machines represent a state-of-the-art paradigm, which
yet has been tackled by a number of other approaches in view of the development
of a superior hybridized technique. It is also the proposal of present chapter to
bring support vector machines together with evolutionary computation, with the
aim to oŽer a simplified solving version for the central optimization problem of
determining the equation of the hyperplane deriving from support vector learning.
The evolutionary approach suggested in this chapter resolves the complexity of the
optimizer, opens the 'black-box' of support vector training and breaks the limits of
the canonical solving component.