Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification

A bid-based approach for coevolving Genetic Programming classifiers is presented. The approach coevolves a population of learners thatdecompose the instance space by way of their aggregate bidding behaviour. To reduce computation overhead, a small, relevant, subsetof training exemplars is (competitively) coevolved alongside the learners. The approach solves multi-class problems using a single population and is evaluated on three large datasets. It is found tobe competitive, especially compared to classifier systems, whilesignificantly reducing the computation overhead associated withtraining.