Introductory Note

The dossier presented in this issue of Interlitteraria analyzes the complex relation between literary and artistic practices, and the situations of censorship and political repression suffered, opposed or responded to, in Lusophone and Hispanophone contexts during the 20th and 21st centuries. Even if many of the case studies deal with Iberian and Ibero-American dictatorships, with this special issue we want to reflect on the concepts of “repression” and “censorship” beyond the scope of authoritarian regimes, for these are processes that manifest themselves in different forms and also in apparently or superficially democratic scenarios. In fact, Iberian and Latin American contexts offer good examples of the persistence of patterns of rejection, repression and censorship in recent times, as well as of the range and potentialities of artistic and creative responses to these patterns. In both cases, we find models and manifestations of “direct violence”, but also more complex and concealed articulations leading to more subtle, and sometimes more dangerous ways, of censorship. From that viewpoint, this special issue discusses topics such as the individual construction of memories of political violence; the reflection of the artists’ own role in a context of repression, or the possibilities of artistic creation as self-affirmation against censorship.info:eu-repo/semantics/publishedVersio

Dynamic Generation of Investment Recommendations Using Grammatical Evolution

The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest

Grammatical evolution-based ensembles for algorithmic trading

The literature on trading algorithms based on Grammatical Evolution commonly presents solutions that rely on static approaches. Given the prevalence of structural change in financial time series, that implies that the rules might have to be updated at predefined time intervals. We introduce an alternative solution based on an ensemble of models which are trained using a sliding window. The structure of the ensemble combines the flexibility required to adapt to structural changes with the need to control for the excessive transaction costs associated with over-trading. The performance of the algorithm is benchmarked against five different comparable strategies that include the traditional static approach, the generation of trading rules that are used for single time period and are subsequently discarded, and three alternatives based on ensembles with different voting schemes. The experimental results, based on market data, show that the suggested approach offers very competitive results against comparable solutions and highlight the importance of containing transaction costs.The authors would like to acknowledge the nancial support of the Spanish Ministry of Science, Innovation and Universities under project PGC2018-646 096849-B-I00 (MCFin)

Evolution of trading strategies with flexible structures: A configuration comparison

Evolutionary Computation is often used in the domain of automated discovery of trading rules. Within this area, both Genetic Programming and Grammatical Evolution offer solutions with similar structures that have two key advantages in common: they are both interpretable and flexible in terms of their structure. The core algorithms can be extended to use automatically defined functions or mechanisms aimed to promote parsimony. The number of references on this topic is ample, but most of the studies focus on a specific setup. This means that it is not clear which is the best alternative. This work intends to fill that gap in the literature presenting a comprehensive set of experiments using both techniques with similar variations, and measuring their sensitivity to an increase in population size and composition of the terminal set. The experimental work, based on three S&P 500 data sets, suggest that Grammatical Evolution generates strategies that are more profitable, more robust and simpler, especially when a parsimony control technique was applied. As for the use of automatically defined function, it improved the performance in some experiments, but the results were inconclusive. (C) 2018 Elsevier B.V. All rights reserved.The authors acknowledge financial support granted by the Spanish Ministry of Science and Innovation under grant ENE2014-56126-C2-2-R

A reactive approach to classifier systems

IEEE International Conference on Systems, Man, and Cybernetics. San Diego, CA, 11-14 Oct. 1998The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and/or sequences of actions. Classifier Systems (CS) have proven their ability of continuous learning, however they have some problems in reactive systems. A modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminating between rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations

Dynamic generation of investment recommendations using grammatical evolution

The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest.The authors would like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin). This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3MXX), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

Los mandatos de conexion y los contratos del sistema garantizado de transmisión: La aplicación del principio de libre acceso a las redes y la colisión de disposiciones

This article has the objective to develop the general framework, previous knowledge and the analysis of the more important aspects related to the controversy that presents the collision of rules between a Connection Order, issued by the Osinergmin, and a Contract of the Guaranteed Transmission System, signed by the Peruvian state and a transmission licensee; that indeed present a unique situation in the energetic policy field.El presente artículo tiene como propósito desarrollar el marco general, nociones previas y análisis de los aspectos más relevantes frente a la controversia que se presenta cuando colisionan las disposiciones de un mandato de conexión, emitido por el Osinergmin, con un contrato del Sistema Garantizado de Transmisión, firmado entre el Estado peruano y un concesionario de transmisión y se presenta una situación única dentro del ámbito normativo energético

Salustiano de Orive (1842-1913). El ingenioso creador del ‘Licor del Polo’

19 páginas.Capítulo incluido en el libro: Ciencia y profesión: el farmacéutico en la historia. Esteban Moreno Toral, Antonio Ramos Carrillo, Antonio González Bueno (eds.). Sevilla, Universidad Internacional de Andalucía, 2018. Págs.: 153-170. Enlace: http://hdl.handle.net/10334/391

A System for Personality and Happiness Detection

This work proposes a platform for estimating personality and happiness. Starting from Eysenck's theory about human's personality, authors seek to provide a platform for collecting text messages from social media (Whatsapp), and classifying them into different personality categories. Although there is not a clear link between personality features and happiness, some correlations between them could be found in the future. In this work, we describe the platform developed, and as a proof of concept, we have used different sources of messages to see if common machine learning algorithms can be used for classifying different personality features and happiness