In this paper a new method is presented to solve a series of multiclass object classification problems using Genetic Programming (GP). All component two-class subproblems of the multiclass 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 with two other GP based methods on four multiclass object classification problems of varying difficulty. The new method outperforms the other methods significantly in terms of both test classification accuracy and training time at the best validation performance in almost all experiments.
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