Bayesian predictive functions for future observations from a generalized Pareto distribution based on generalized order statistics are obtained. Two cases are considered unknown one parameter and unknown two parameters. We also consider two cases fixed sample size and random sample size. The Bayesian predictive functions are specialized to ordinary order statistics, progressive type II censorin...

Previous works have shown the eeciency of a new approach for the Genetic Algorithms, the Dual Genetic Algorithms, in the multiobjective optimization context. Dual Genetic Algorithms make use of a meta level to enhance the expressiveness of schemata, entities implicitly handle by Genetic Algorithms. In this paper, we show that this approach, coupled with a new method, Pareto Elitism, leads to ve...

This paper investigates the performance of evolutionary algorithms in the optimization aspects of oblique decision tree construction and describes their performance with respect to classification accuracy, tree size, and Pareto-optimality of their solution sets. The performance of the evolutionary algorithms is analyzed and compared to the performance of exhaustive (traditional) decision tree c...

Learning systems (also known as Pittsburgh learning classifier systems) need to balance accuracy and parsimony for evolving high quality general hypotheses. The evolutionary learning process used in learning systems is based on using a set of training instances that sample the target concept to be learned. Thus, the the learning process may overfit the learned hypothesis to the given set of tra...

We study Kolmogorov-Smirnov goodness of fit tests for evaluating distributional hypotheses where unknown parameters need to be fitted. Following work of Pollard (1979), our approach uses a Cramérvon Mises minimum distance estimator for parameter estimation. The asymptotic null distribution of the resulting test statistic is represented by invariance principle arguments as a functional of a Brow...

trees are binary, split fit is co-Pareto on components (Wilkinson et al., 2005a). When input trees have polytomies, an optimal split fit supertree could include arbitrary resolutions that do not contradict any displayed splits. In that case, split fit can fail to be co-Pareto (but not sub-Pareto) on components. In contrast, the corresponding strict consensus, the split fit* supertree, suppresse...