Benchmarking coevolutionary teaming under classification problems with large attribute spaces

Abstract

Benchmarking of a team based model of Genetic Programming demonstrates that the naturally embedded style of feature selection is usefully extended by the teaming metaphor to provide solutions in terms of exceptionally low attribute counts. To take this concept to its logical conclusion the teaming model must be able to build teams with a non-overlapping behavioral trait, from a single population. The Symbiotic Bid-Based (SBB) algorithm is demonstrated to fit this purpose under an evaluation utilizing data sets with 650 to 5,000 attributes. The resulting solutions are one to two orders simpler than solutions identified under the alternative embedded paradigms of C4.5 and MaxEnt.