Within the present paper, we put forward a novel hybridization between
support vector machines and evolutionary algorithms. Evolutionary
support vector machines consider the classification task as in
support vector machines but use an evolutionary algorithm to solve
the optimization problem of determining the decision function.
They can explicitly acquire the coefficients of the separating
hyperplane, which is often not possible within the classical
technique. More important, evolutionary support vector machines
obtain the coefficients directly from the evolutionary algorithm
and can refer them at any point during a run. In addition, they do
not require properties of positive (semi-)definition for kernels
within nonlinear learning. The concept can be furthermore extended
to handle large amounts of data, a problem frequently occurring
e.g. in spam mail detection, one of our test cases. An adapted
chunking technique is therefore alternatively used. In addition to
two different representations, a crowding variant of the
evolutionary algorithm is tested in order to investigate whether
the performance of the algorithm is maintained; its global search
capabilities would be important for the prospected coevolution of
non-standard kernels. Evolutionary support vector machines are
validated on four real-world classification tasks; obtained
results show the promise of this new approach.