abstract = "In this paper a method is presented to solve a series
of multiple-class object classification problems using
Genetic Programming (GP). All component two-class
subproblems of the multiple-class problem are solved in
a single run, using a multi-objective fitness function.
Probabilistic methods are used, with each evolved
program required to solve only one subproblem. Programs
gain a fitness related to their rank at the subproblem
that they solve best. The new method is compared by
experiment to two other GP based methods on four
multiple-class classification problems of varying
difficulty. The new method outperforms the other
methods significantly in almost all experiments. The
new method often takes a longer running time, but
usually reaches a peak in accuracy very early.",