Principled quality diversity for ensemble classifiers using MAP-Elites
Kyle Nickerson, Ting Hu · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2021
For many supervised learning tasks, ensemble classifiers - which make predictions by combining multiple simple models - outperform single model classifiers. While genetic programming can be used to evolve populations of simple classifiers, it tends to produce populations of highly similar models. In this work we propose Neuro MAP-Elites (NME) as a method for evolving populations of high performing models which produce diverse predictions, making them suitable for constructing ensembles.