Rule Extraction by Genetic Programming with Clustered Terminal Symbols

When Genetic Programming (GP) is applied to rule extraction from databases, the attributes of the data are often used for the terminal symbols. However, in the case of the database with a large number of attributes, the search space becomes vast because the size of the terminal set increases. As a result, the search performance declines. For improving the search performance, we propose new methods for dealing with the large-scale terminal set. In the methods, the terminal symbols are clustered based on the similarities of the attributes. In the beginning of search, by reducing the number of terminal symbols, the rough and rapid search is performed. In the latter stage of search, by using the original attributes for terminal symbols, the local search is performed. By comparison with the conventional GP, the proposed methods showed the faster evolutional speed and extracted more accurate classification rules

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems

Enterprise information integration: on discovering links using genetic programming

Both established and emergent business rely heavily on data, chiefly those that wish to become game changers. The current biggest source of data is the Web, where there is a large amount of sparse data. The Web of Data aims at providing a unified view of these islands of data. To realise this vision, it is required that the resources in different data sources that refer to the same real-world entities must be linked, which is they key factor for such a unified view. Link discovery is a trending task that aims at finding link rules that specify whether these links must be established or not. Currently there are many proposals in the literature to produce these links, especially based on meta-heuristics. Unfortunately, creating proposals based on meta-heuristics is not a trivial task, which has led to a lack of comparison between some well-established proposals. On the other hand, it has been proved that these link rules fall short in cases in which resources that refer to different real-world entities are very similar or vice versa. In this dissertation, we introduce several proposals to address the previous lacks in the literature. On the one hand we, introduce Eva4LD, which is a generic framework to build genetic programming proposals for link discovery; which are a kind of meta-heuristics proposals. Furthermore, our framework allows to implement many proposals in the literature and compare their results fairly. On the other hand, we introduce Teide, which applies effectively the link rules increasing significantly their precision without dropping their recall significantly. Unfortunately, Teide does not learn link rules, and applying all the provided link rules is computationally expensive. Due to this reason we introduce Sorbas, which learns what we call contextual link rules.Las empresas que desean establecer un precedente en el panorama actual tienden a recurrir al uso de datos para mejorar sus modelos de negocio. La mayor fuente de datos disponible es la Web, donde una gran cantidad es accesible aunque se encuentre fragmentada en islas de datos. La Web de los Datos tiene como objetivo dar una visión unificada de dichas islas, aunque el almacenamiento de los mismos siga siendo distribuido. Para ofrecer esta visión es necesario enlazar los recursos presentes en las islas de datos que hacen referencia a las mismas entidades del mundo real. Link discovery es el nombre atribuido a esta tarea, la cual se basa en generar reglas de enlazado que permiten establecer bajo qué circunstancias dos recursos deben ser enlazados. Se pueden encontrar diferentes propuestas en la literatura de link discovery, especialmente basadas en meta-heurísticas. Por desgracia comparar propuestas basadas en meta-heurísticas no es trivial. Por otro lado, se ha probado que estas reglas de enlazado no funcionan bien cuando los recursos que hacen referencia a dos entidades distintas del mundo real son muy parecidos, o por el contrario, cuando dos recursos muy distintos hacen referencia a la misma entidad. En esta tesis presentamos varias propuestas. Por un lado, Eva4LD es un framework genérico para desarrollar propuestas de link discovery basadas en programación genética, que es un tipo de meta-heurística. Gracias a nuestro framework, hemos podido implementar distintas propuestas de la literatura y comprar justamente sus resultados. Por otro lado, en la tesis presentamos Teide, una propuesta que recibiendo varias reglas de enlazado las aplica de tal modo que mejora significativamente la precisión de las mismas sin reducir significativamente su cobertura. Por desgracia, Teide es computacionalmente costoso debido a que no aprende reglas. Debido a este motivo, presentamos Sorbas que aprende un tipo de reglas de enlazado que denominamos reglas de enlazado con contexto

Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors from computers. One of its most prolific subfields is that of Music Generation (also called Algorithmic Composition or Musical Metacreation), that uses computational means to compose music. Due to the multidisciplinary nature of this research field, it is sometimes hard to define precise goals and to keep track of what problems can be considered solved by state-of-the-art systems and what instead needs further developments. With this survey, we try to give a complete introduction to those who wish to explore Computational Creativity and Music Generation. To do so, we first give a picture of the research on the definition and the evaluation of creativity, both human and computational, needed to understand how computational means can be used to obtain creative behaviors and its importance within Artificial Intelligence studies. We then review the state of the art of Music Generation Systems, by citing examples for all the main approaches to music generation, and by listing the open challenges that were identified by previous reviews on the subject. For each of these challenges, we cite works that have proposed solutions, describing what still needs to be done and some possible directions for further research

Studying the Functional Genomics of Stress Responses in Loblolly Pine With the Expresso Microarray Experiment Management System

Conception, design, and implementation of cDNA microarray experiments present a variety of bioinformatics challenges for biologists and computational scientists. The multiple stages of data acquisition and analysis have motivated the design of Expresso, a system for microarray experiment management. Salient aspects of Expresso include support for clone replication and randomized placement; automatic gridding, extraction of expression data from each spot, and quality monitoring; flexible methods of combining data from individual spots into information about clones and functional categories; and the use of inductive logic programming for higher-level data analysis and mining. The development of Expresso is occurring in parallel with several generations of microarray experiments aimed at elucidating genomic responses to drought stress in loblolly pine seedlings. The current experimental design incorporates 384 pine cDNAs replicated and randomly placed in two specific microarray layouts. We describe the design of Expresso as well as results of analysis with Expresso that suggest the importance of molecular chaperones and membrane transport proteins in mechanisms conferring successful adaptation to long-term drought stress

Challenges of ELA-guided Function Evolution using Genetic Programming

Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years. Within topics such as instance space analysis (ISA), the generation of new problems can provide new benchmarks which are not yet explored in existing research. Beyond that, this function generation can also be exploited for solving complex real-world optimization problems. By generating functions with similar properties to the target problem, we can create a robust test set for algorithm selection and configuration. However, the generation of functions with specific target properties remains challenging. While features exist to capture low-level landscape properties, they might not always capture the intended high-level features. We show that a genetic programming (GP) approach guided by these exploratory landscape analysis (ELA) properties is not always able to find satisfying functions. Our results suggest that careful considerations of the weighting of landscape properties, as well as the distance measure used, might be required to evolve functions that are sufficiently representative to the target landscape