Forming classifier ensembles with multimodal evolutionary algorithms 

Abstract

Ensemble classifiers have become popular in recent years owing to their ability to produce robust 
predictive models that generalise well to previously unseen data. In principle, Evolutionary Algorithms 
(EAs) are well suited to ensemble generation since they result in a pool of trained classifiers. However, 
in practice they are infrequently used for this purpose. Current research trends in the EA community focus 
on relatively complex mechanisms for building ensembles, such as co-evolution and multi-objective optimisation. 
In this paper, we take a back-to-basics approach, studying whether conventional EAs, augmented with simple 
niching strategies, can be used to form accurate ensembles. We focus on crowding for this, considering both 
deterministic and probabilistic variants. We also consider the effect of different similarity measures. Our 
results suggest that simple niching methods can lead to accurate ensemble classifiers and that the choice of 
similarity measure is not a significant factor. A further study using heterogeneous classifier models within 
the population showed no added benefit.