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

131 p.: graf.

Análisis del comportamiento del CTC con problemas de clases muy desbalanceadas del repositorio KEEL

En este proyecto se analiza y compara el comportamiento del algoritmo CTC diseñado por el grupo de investigación ALDAPA usando bases de datos muy desbalanceadas. En concreto se emplea un conjunto de bases de datos disponibles en el sitio web asociado al proyecto KEEL (http://sci2s.ugr.es/keel/index.php) y que han sido ya utilizadas con diferentes algoritmos diseñados para afrontar el problema de clases desbalanceadas (Class imbalance problem) en el siguiente trabajo: A. Fernandez, S. García, J. Luengo, E. Bernadó-Mansilla, F. Herrera, "Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy and Comparative Study". IEEE Transactions on Evolutionary Computation 14:6 (2010) 913-941, http://dx.doi.org/10.1109/TEVC.2009.2039140 Las bases de datos (incluidas las muestras del cross-validation), junto con los resultados obtenidos asociados a la experimentación de este trabajo se pueden encontrar en un sitio web creado a tal efecto: http://sci2s.ugr.es/gbml/. Esto hace que los resultados del CTC obtenidos con estas muestras sean directamente comparables con los obtenidos por todos los algoritmos obtenidos en este trabajo.

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.

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

14 p.

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

208 p.

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.

Systematic and evolutionary studies of the terrestrial gastropods Helicoidea and Pyramidula

178 p.