Evolutionary framework for the construction of diverse hybrid ensembles

Enforcing diversity explicitly in ensembles while at the same time making individual predictors accurate as well has been shown to be promising. This idea was recently taken into account in the algorithm DIVACE. There have been a multitude of theories on how one can enforce diversity within a combined predictor setup. This paper aims to bring these theories together in an attempt to synthesise a framework that can be used to engender new evolutionary ensemble learning algorithms. The framework treats diversity and accuracy as evolutionary pressures that can be exerted at multiple levels of abstraction and is shown to be effective.