A Study in function optimization with the breeder genetic algorithm

Optimization is concerned with the finding of global optima (hence the name) of problems that can be cast in the form of a function of several variables and constraints thereof. Among the searching methods, {em Evolutionary Algorithms} have been shown to be adaptable and general tools that have often outperformed traditional {em ad hoc} methods. The {em Breeder Genetic Algorithm} (BGA) combines a direct representation with a nice conceptual simplicity. This work contains a general description of the algorithm and a detailed study on a collection of function optimization tasks. The results show that the BGA is a powerful and reliable searching algorithm. The main discussion concerns the choice of genetic operators and their parameters, among which the family of Extended Intermediate Recombination (EIR) is shown to stand out. In addition, a simple method to dynamically adjust the operator is outlined and found to greatly improve on the already excellent overall performance of the algorithm.Postprint (published version

Bayesian semi non-negative matrix factorisation

Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow for negative-valued data and sources in the form of Semi-and Convex-NMF. In this paper, we re-elaborate Semi-NMF within a full Bayesian framework. This provides solid foundations for parameter estimation and, importantly, a principled method to address the problem of choosing the most adequate number of sources to describe the observed data. The proposed Bayesian Semi-NMF is preliminarily evaluated here in a real neuro-oncology problem.Peer ReviewedPostprint (published version

Instance and feature weighted k-nearest-neighbors algorithm

We present a novel method that aims at providing a more stable selection of feature subsets when variations in the training process occur. This is accomplished by using an instance-weighting process -assigning different importances to instances as a preprocessing step to a feature weighting method that is independent of the learner, and then making good use of both sets of computed weigths in a standard Nearest-Neighbours classifier. We report extensive experimentation in well-known benchmarking datasets as well as some challenging microarray gene expression problems. Our results show increases in stability for most subset sizes and most problems, without compromising prediction accuracy.Peer ReviewedPostprint (published version

Heterogeneous Kohonen networks

A large number of practical problems involves elements that are described as a mixture of qualitative and quantitative infomation, and whose description is probably incomplete. The self-organizing map is an effective tool for visualization of high-dimensional continuous data. In this work, we extend the network and training algorithm to cope with heterogeneous information, as well as missing values. The classification performance on a collection of benchmarking data sets is compared in different configurations. Various visualization methods are suggested to aid users interpret post-training results.Peer ReviewedPostprint (author's final draft

Feature selection algorithms: a survey and experimental evaluation

In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work reviews several fundamental algorithms found in the literature and assesses their performance in a controlled scenario. A scoring measure ranks the algorithms by taking into account the amount of relevance, irrelevance and redundance on sample data sets. This measure computes the degree of matching between the output given by the algorithm and the known optimal solution. Sample size effects are also studied.Postprint (published version

Linguloidean brachiopods from the Lower Ordovician (Tremadocian) of northwestern Argentina

The new obolid Torobolus subplanus gen. et sp. nov., from the lower Temadocian Devendeus Formation, the new species Libecoviella tilcarensis and Leptembolon argentinum, and Ectenoglossa sp. from upper Tremadocian beds of the Santa Rosita Formation are described and ilustrated. Libecoviella is typical of the upper Tremadocian and Floian strata of the Prague basin (Trenice and Klabava formations, respectively) and it has been reported recently from Australia. Leptembolon has been recorded in the same Bohemian formations, but together with other taxa it forms the Thysanotos-Leptembolon Association present in northern Estonia and a series of high-latitude terranes. The record of Leptembolon and Libecoviella in the high- to temperate-latitude Central Andean region attests for a peri-Gondwanan distribution of these genera. The presence of Bohemian-like obolids in northwestern Argentina suggests a migratory route linking the Central Andean basin with north Gondwana and Perunica along the clastic platforms fringing the North African and Brazilian shieldsFil: Benedetto, Juan Luis Arnaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones en Ciencias de la Tierra. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones en Ciencias de la Tierra; ArgentinaFil: Muñoz, Diego Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones en Ciencias de la Tierra. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones en Ciencias de la Tierra; Argentin