Sistema Evolutivo Bio-inspirado en el Comportamiento Bacteriano

The main goal of this thesis is to build a new evolutionary computation method that implements an asynchronous, decentralized and continuous general-purpose evolutionary process designed to automatically generate intelligent systems. The grammar-guided evolutionary automatic system (GGEAS) is an evolutionary framework with a modular design that is capable of adapting grammar-guided genetic programming techniques for use in the construction of intelligent systems for different application domains. GGEAS has been used to automatically build symbolic and sub-symbolic intelligent systems, as well as synthetic biological circuits. The artificial bacterium is an evolutionary vehicle bio-inspired by the behavior of bacteria in nature. This bacterium uses a derivation tree belonging to a context-free grammar to codify an intelligent system in its inside. The artificial bacterium constantly and asynchronously evolves this intelligent system as it moves within a constantly changing simulated 3D environment. Additionally, this research defines the conjugation operator and the quorum sensing population control method. The conjugation operator uses a grammar-based crossover operator to cross the inner intelligent systems of two artificial bacteria that are close together in the environment. The quorum sensing control method implements a distributed artificial bacterial population control mechanism. The combined application of the techniques developed in this thesis builds the bacterially inspired evolutionary system. This system takes inspiration from the natural behavior of bacterial populations to build a distributed, asynchronous and parallel evolutionary mechanism that preserves AGGES strengths and is capable of automatically generating intelligent systems in constantly changing environments

Generación de sistemas basados en reglas mediante programación genética

El objetivo fundamental de esta tesis de fin de máster es la construcción de un algoritmo de generación automática de sistemas basados en reglas mediante técnicas evolutivas, y su aplicación a la resolución del problema de detección de lesiones de rodilla a partir de curvas isocinéticas. Se presentan dos técnicas diferentes de generación de sistemas basados en reglas a través de programación genética guiada por gramáticas: la primera genera directamente sistemas basados en reglas y la segunda genera indirectamente sistemas basados en reglas difusas representados a través de redes de neuronas difusas. Se introduce un sistema de codificación de individuos específico de cada técnica, una gramática libre de contexto que permite la generación de individuos sujetos a dicha codificación y un método de evaluación de individuos especializado para el problema de detección de lesiones de rodilla. Asimismo, se presenta un nuevo método de análisis de series temporales de longitud variable que permite convertir una curva isocinética en un vector de dimensión finita, procesable por los generadores automáticos de sistemas basados en reglas. La aplicación de las técnicas desarrolladas en esta tesis permite la construcción de sistemas basados en reglas y sistemas basados en reglas difusas, a partir de un conjunto de datos de entrenamiento pertenecientes a un dominio de aplicación cualquiera. Estas técnicas permiten la generación de bases de conocimiento de forma automática reduciendo el coste asociado a los métodos tradicionales de educción de conocimientos, los cuales son altamente dependientes del experto del dominio. Los resultados de investigación presentados en este trabajo suponen un avance dentro del área relacionada con la construcción de sistemas inteligentes robustos: sistemas capaces de adaptarse a diferentes dominios o a los cambios que se puedan producir, facilitando el proceso de mantenimiento y actualización constante de una base de conocimiento

Towards the automatic generation of card games through Grammar-Guided Genetic Programming

We demonstrate generating complete and playable card games using evolutionary algorithms. Card games are represented in a previously devised card game description language, a context-free grammar. The syntax of this language allows us to use grammar-guided genetic programming. Candidate card games are evaluated through a cascading evaluation function, a multi-step process where games with undesired properties are progressively weeded out. Three representa- tive examples of generated games are analysed. We observed that these games are reasonably balanced and have skill ele- ments, they are not yet entertaining for human players. The particular shortcomings of the examples are discussed in re- gard to the generative process to be able to generate quality game

Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems

Classical approaches when building diagnosis and monitoring systems are rule-based systems, which allow the representation of existing knowledge by using rules. There are several techniques that facilitate this task, such as fuzzy logic, which allows knowledge to be modeled in an intuitive way. Nevertheless, sometimes it is not easy to define the fuzzy rule set that represents the knowledge from a certain domain. To overcome this drawback, an evolutionary system based on a grammar guided genetic programming technique for the automatic generation of fuzzy knowledge bases has been employed in diagnosing monitored railway networks. This system employs a grammar-based initialization method and both, grammar-based crossover and mutation operators, to achieve well balanced exploitation and exploration capabilities of the search space, assuring high convergence speed and low chance of getting trapped in local optima. Tests have been carried out in a real-world train monitoring domain, in which a sensor network is periodically monitoring critical train components. Results achieved show that this evolutionary system accomplishes an automatic knowledge discovery process, which is able to build a fuzzy rule base that represents the expert knowledge extracted from the domain of the detection of abnormal train conditions

Genetic Diversity in Local Spanish Olive Cultivars Assessed by Molecular Characterization with : abstract

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Physicochemical, organoleptic and molecular analysis of two varieties of peach within the protected designation of origin (DOP) “Calanda” during storage at different temperatures: abstract

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Innovation project to validate and select items for assessing transversal competencies in higher education

[EN] The educational improvement innovation project is focused on determining how to evaluate any competence and which are their main related items that should be used for that purpose. Currently, the selection of items is usually performed by groups of experts. However, two main problems related to this type of selection arise in this case: on the one hand, the selection resulting from different groups of experts may be not the same or similar enough, since it is based on the experience and knowledge of each member. On the other hand, the coefficients or weights a priori assigned to each item on any competence invalidate any a posteriori analysis on its statistical significance and the "real" weight on this competence. To mitigate the above drawbacks, this work presents a methodology able to select, from an objective point of view, the items related to a specific competence, from a set of potentially related ones; furthermore the weights associated to the items are determined. This is carried out by applying a multivariate statistical projection method such as Partial Least Squares (PLS), embedded in a cross-validation process. The paper presents how to preprocess the data, analyze it and obtain the items and their weights to be used for the evaluation of a specific competence.Prats-Montalbán, JM.; Alarcón Valero, F.; Alemany Díaz, MDM.; Boza, A.; Gordo Monzó, ML.; Fernández-Diego, M.; Ruiz Font, L.... (2016). Innovation project to validate and select items for assessing transversal competencies in higher education. ICERI Proceedings. 61-68. doi:10.21125/iceri.2016.1012S616

Genetic diversity in local Spanish pear cultivars assessed by molecular characterization with SSRs

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