Abstract The hybrid genetic search (HGS) metaheuristic has produced outstanding results for several variants of the vehicle routing problem. A recent implementation of HGS specialized to the capacitated vehicle routing problem (CVRP) is a state‐of‐the‐art method for this variant. This paper proposes an improved HGS for the CVRP obtained by incorporating a new solution generation method into its (re‐)initialization process to guide the search more efficiently and effectively. The solution generation method introduced in this work combines an approach based on frequent patterns extracted from good solutions by a data mining process and a randomized version of the Clarke and Wright savings heuristic. As observed in our experimental comparison, the proposed method significantly outperforms the original algorithm regarding the final gap to the best known solutions and the primal integral.

Data uncertainty remains a challenging issue in many applications, but few classification algorithms can effectively cope with it. An ensemble approach for uncertain categorical features has recently been proposed, achieving promising results. It consists in biasing the sampling of features for each model in an ensemble so that less uncertain features are more likely to be sampled. Here we extend this idea of biased sampling and propose two new approaches: one for selecting training instances for each model in an ensemble and another for sampling features to be considered when splitting a node in a Random Forest training. We applied these approaches to classify ageing-related genes and predict drugs' side effects based on uncertain features representing protein-protein and protein-chemical interactions. We show that ensembles based on our proposed approaches achieve better predictive performance. In particular, our proposed approaches improved the performance of a Random Forest based on the most sophisticated approach for handling uncertain data in ensembles of this kind. Furthermore, we propose two new approaches for interpreting an ensemble of Naive Bayes classifiers and analyse their results on our datasets of ageing-related genes and drug's side effects.

Coping with uncertainty is a very challenging issue in many real-world applications. However, conventional classification models usually assume there is no uncertainty in data at all. In order to fill this gap, there has been a growing number of studies addressing the problem of classification based on uncertain data. Although some methods resort to ignoring uncertainty or artificially removing it from data, it has been shown that predictive performance can be improved by actually incorporating information on uncertainty into classification models. This paper proposes an approach for building an ensemble of classifiers for uncertain categorical data based on biased random subspaces. Using Naive Bayes classifiers as base models, we have applied this approach to classify ageing-related genes based on real data, with uncertain features representing protein-protein interactions. Our experimental results show that models based on the proposed approach achieve better predictive performance than single Naive Bayes classifiers and conventional ensembles.