Abstract. The EENCL algorithm [1] automatically designs neural network ensembles for classification, combining global evolution with local search based on gradient descent. Two mec...
In this work we study how using multiple communicating populations instead of a single panmictic one may help in maintaining diversity during GP runs. After defining suitable geno...
Marco Tomassini, Leonardo Vanneschi, Francisco Fer...
The concept of diversity was successfully introduced for recommender-systems. By displaying results that are not only similar to a target problem but also diverse among themselves,...
Ensemble of Classifiers (EoC) has been shown effective in improving the performance of single classifiers by combining their outputs. By using diverse data subsets to train classi...
Albert Hung-Ren Ko, Robert Sabourin, Luiz E. Soare...
The cultural diversity of users of technology challenges our methods for usability evaluation. In this paper we report on a multi-site, cross-cultural grounded theory field study o...