Recently there have been advances in strati ed sampling techniques that attempt to enforce equal distributions not only across the design variables, but also onto the design space itself. This requires a numerically intensive optimization routine. Until now, no optimization strategy was able to distribute sample points evenly in the design space, but Evolutionary Algorithms (EA) act as an enabl...

A method for finding probable phylogenetic gene branchings is presented. The events that caused the branchings are estimated by using probabilistic orthology analysis. The orthology analysis is based on a model for gene evolution in a species phylogeny. Markov chain Monte Carlo sampling is used to sample gene phylogenies from an a posteriori distribution after having observed gene sequence data...

Uncertainties are inherent in engineering problems due to various numerical modeling “imperfections’’ and due to the inevitable scattering of the design parameters from their nominal values. Under this perspective, there are two main optimal design formulations that account for the probabilistic response of structural systems: Reliability-based Design Optimization (RBDO) and Robust Design Optim...

The distributed evolutionary computation platform EC-Star is extended in this paper to probabilistic classifiers. This extension, called PRETSL, allows the distributed age-layered evolution of probabilistic rule sets, which in turn makes more fine-grained decisions possible. The method is tested on 20 UCI data problems, as well as a larger dataset of arterial blood pressure waveforms. The Resul...

The idea of exploiting Global Sensitivity Analysis (GSA) to make Evolutionary Algorithms more effective seems very attractive: intuitively, a probabilistic analysis can prove useful to a stochastic optimisation technique. GSA, that gathers information about the behaviour of functions receiving some inputs and delivering one or several outputs, is based on computationally-intensive stochastic sa...

Biogeography-based optimization (BBO) is a population-based evolutionary algorithm (EA) that is based on the mathematics of biogeography. Biogeography is the study of the geographical distribution of biological organisms. We present a simplified version of BBO and perform an approximate analysis of the BBO population using probability theory. Our analysis provides approximate values for the exp...

In order to deal with a large number of attributes, probabilistic feature selection algorithms have been proposed. Pure random walk entails mediocre performance in search time. Introducing adaptiveness into a probabilistic algorithm can lead to more focused search that means better search time. We compare two algorithms here in search of an efficient but not myopic algorithm for feature selecti...

Due to correlation coefficient matrix of initialized samples are not always positive definite, this paper presents the improved Latin Hypercube Sampling (LHS) methods with Evolutionary Algorithm (EA) to control correlation and handle power system probability analysis problem. To deal with the non-positive definite correlation matrix, an improved median Latin hypercube sampling with evolutionary...

As whole-genome protein-protein interaction datasets become available for a wide range of species, evolutionary biologists have the opportunity to address some of the unanswered questions surrounding the evolution of these complex systems. Protein interaction networks from divergent organisms may be compared to investigate how gene duplication, deletion, and rewiring processes have shaped the e...