Novel ways of improving cooperation and performance in ensemble classifiers

There are two common methods of evolving teams of genetic programs. Research suggests Island approaches produce teams of strong individuals that cooperate poorly and Team approaches produce teams of weak individuals that cooperate strongly. Ideally, teams should be composed of strong individuals that cooperate well. In this paper we present a new class of algorithms called Orthogonal Evolution of Teams (OET) that overcomes the weaknesses of current Island and Team approaches by applying evolutionary pressure at both the level of teams and individuals during selection and replacement. We present four novel algorithms in this new class and compare their performance to Island and Team approaches as well as multi-class Adaboost on a number of classification problems.