abstract = "A hybrid evolutionary technique is proposed for data
mining tasks, which combines the Clonal Selection
Principle with Gene Expression Programming (GEP). The
proposed algorithm introduces the notion of Data Class
Antigens, which is used to represent a class of data.
The produced rules are evolved by a clonal selection
algorithm, which extends the recently proposed CLONALG
algorithm. In the present algorithm, among other new
features, a receptor editing step has been
incorporated. Moreover, the rules themselves are
represented as antibodies, which are coded as GEP
chromosomes, in order to exploit the flexibility and
the expressiveness of such encoding. The algorithm is
tested on some benchmark problems of the UCI
repository, and in particular on the set of MONK
problems and the Pima Indians Diabetes problem. In both
problems, the results in terms of prediction accuracy
are very satisfactory, albeit slightly less accurate
than those obtained by a standard GEP technique. In
terms of convergence rate and computational efficiency,
however, the technique proposed here markedly
outperforms the standard GEP algorithm.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.