Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.

Algoritmos multi-objetivos para la optimización de una secuencia de matrices generadoras en computación cuántica topológica

Quantum Computing is a relatively modern field which simulates quantum computation conditions. Moreover, it can be used to estimate which quasiparticles would endure better in a quantum environment. Topological Quantum Computing (TQC) is an approximation for reducing the quantum decoherence problem1, which is responsible for error appearance in the representation of information. This project tackles specific instances of TQC problems using MOEAs (Multi-objective Optimization Evolutionary Algorithms). A MOEA is a type of algorithm which will optimize two or more objectives of a problem simultaneously, using a population based approach. We have implemented MOEAs that use probabilistic procedures found in EDAs (Estimation of Distribution Algorithms), since in general, EDAs have found better solutions than ordinary EAs (Evolutionary Algorithms), even though they are more costly. Both, EDAs and MOEAs are population-based algorithms. The objective of this project was to use a multi-objective approach in order to find good solutions for several instances of a TQC problem. In particular, the objectives considered in the project were the error approximation and the length of a solution. The tool we used to solve the instances of the problem was the multi-objective framework PISA. Because PISA has not too much documentation available, we had to go through a process of reverse-engineering of the framework to understand its modules and the way they communicate with each other. Once its functioning was understood, we began working on a module dedicated to the braid problem. Finally, we submitted this module to an exhaustive experimentation phase and collected results.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

A Behavioral Foundation for Models of Evolutionary Drift

Binmore and Samuelson (1999) have shown that perturbations (drift) are crucial to study the stability properties of Nash equilibria. We contribute to this literature by providing a behavioural foundation for models of evolutionary drift. In particular, this article introduces a microeconomic model of drift based on the similarity theory developed by Tversky (1977), Kahneman and Tversky (1979) and Rubinstein (1988),(1998). An innovation with respect to those works is that we deal with similarity relations that are derived from the perception that each agent has about how well he is playing the game. In addition, the similarity relations are adapted to a dynamic setting. We obtain different models of drift depending on how we model the agent´s assessment of his behaviour in the game. The examples of the ultimatum game and the chain-store game are used to show the conditions for each model to stabilize elements in the component of Nash equilibria that are not subgame- perfect. It is also shown how some models approximate the laboratory data about those games while others match the data.

Replicator Dynamics and Evolutionary Stable Strategies in Heterogeneous Games

We generalise and extend the work of Iñarra and Laruelle (2011) by studying two person symmetric evolutionary games with two strategies, a heterogenous population with two possible types of individuals and incomplete information. Comparing such games with their classic homogeneous version vith complete information found in the literature, we show that for the class of anti-coordination games the only evolutionarily stable strategy vanishes. Instead, we find infinite neutrally stable strategies. We also model the evolutionary process using two different replicator dynamics setups, each with a different inheritance rule, and we show that both lead to the same results with respect to stability.

Avances en algoritmos de estimación de distribuciones. Alternativas en el aprendizaje y representación de problemas

131 p.: graf.

New signal processing algorithms for automated external defibrillators

[ES]La fibrilación ventricular (VF) es el primer ritmo registrado en el 40\,\% de las muertes súbitas por paro cardiorrespiratorio extrahospitalario (PCRE). El único tratamiento eficaz para la FV es la desfibrilación mediante una descarga eléctrica. Fuera del hospital, la descarga se administra mediante un desfibrilador externo automático (DEA), que previamente analiza el electrocardiograma (ECG) del paciente y comprueba si presenta un ritmo desfibrilable. La supervivencia en un caso de PCRE depende fundamentalmente de dos factores: la desfibrilación temprana y la resucitación cardiopulmonar (RCP) temprana, que prolonga la FV y por lo tanto la oportunidad de desfibrilación. Para un correcto análisis del ritmo cardiaco es necesario interrumpir la RCP, ya que, debido a las compresiones torácicas, la RCP introduce artefactos en el ECG. Desafortunadamente, la interrupción de la RCP afecta negativamente al éxito en la desfibrilación. En 2003 se aprobó el uso del DEA en pacientes entre 1 y 8 años. Los DEA, que originalmente se diseñaron para pacientes adultos, deben discriminar de forma precisa las arritmias pediátricas para que su uso en niños sea seguro. Varios DEAs se han adaptado para uso pediátrico, bien demostrando la precisión de los algoritmos para adultos con arritmias pediátricas, o bien mediante algoritmos específicos para arritmias pediátricas. Esta tesis presenta un nuevo algoritmo DEA diseñado conjuntamente para pacientes adultos y pediátricos. El algoritmo se ha probado exhaustivamente en bases de datos acordes a los requisitos de la American Heart Association (AHA), y en registros de resucitación con y sin artefacto RCP. El trabajo comenzó con una larga fase experimental en la que se recopilaron y clasificaron retrospectivamente un total de 1090 ritmos pediátricos. Además, se revisó una base de arritmias de adultos y se añadieron 928 nuevos ritmos de adultos. La base de datos final contiene 2782 registros, 1270 se usaron para diseñar el algoritmo y 1512 para validarlo. A continuación, se diseñó un nuevo algoritmo DEA compuesto de cuatro subalgoritmos. Estos subalgoritmos están basados en un conjunto de nuevos parámetros para la detección de arritmias, calculados en diversos dominios de la señal, como el tiempo, la frecuencia, la pendiente o la función de autocorrelación. El algoritmo cumple las exigencias de la AHA para la detección de ritmos desfibrilables y no-desfibrilables tanto en pacientes adultos como en pediátricos. El trabajo concluyó con el análisis del comportamiento del algoritmo con episodios reales de resucitación. En los ritmos que no contenían artefacto RCP se cumplieron las exigencias de la AHA. Posteriormente, se estudió la precisión del algoritmo durante las compresiones torácicas, antes y después de filtrar el artefacto RCP. Para suprimir el artefacto se utilizó un nuevo método desarrollado a lo largo de la tesis. Los ritmos desfibrilables se detectaron de forma precisa tras el filtrado, los no-desfibrilables sin embargo no.

Algorithms for colour image processing based on neurological models

373 p. : il., gráf., fot., tablas

Water quality assessment and characterisation of chlorophyll-a variability related to river discharges, within the southeastern Bay of Biscay : evaluation and development of chlorophyll-a algorithms for MODIS and MERIS imagery

222 p. : il.

Framework for the Shape Optimization of Aerodynamic Profiles Using Genetic Algorithms

This study developed a framework for the shape optimization of aerodynamics profiles using computational fluid dynamics (CFD) and genetic algorithms. Agenetic algorithm code and a commercial CFD code were integrated to develop a CFD shape optimization tool. The results obtained demonstrated the effectiveness of the developed tool. The shape optimization of airfoils was studied using different strategies to demonstrate the capacity of this tool with different GA parameter combinations.

The First Occurrence in the Fossil Record of an Aquatic Avian Twig-Nest with Phoenicopteriformes Eggs: Evolutionary Implication

14 p.

Extending Distance-based Ranking Models In Estimation of Distribution Algorithms

Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall’s-t distance, have demonstrated their validity when solving this type of problems. Nevertheless, there are still many trends that deserve further study. In this paper, we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam. In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall, Cayley and Ulam distances, we run them on a benchmark of 120 instances from four well known permutation problems. The conducted experiments showed that there is not just one metric that performs the best in all the problems. However, the statistical test pointed out that Mallows-Ulam EDA is the most stable algorithm among the studied proposals.

On parallel computing for stochastic optimization models and algorithms

167 p.

Molecular ecology of european mustelids: Unraveling evolutionary and ecological patterns in Martes and Lutra genera

208 p.

Contributions on distance-based algorithms, multi-classifier construction and pairwise classification

179 p.