J. J. Merelo

Sérgio M. Rebelo, J. J. Merelo, João Bicker et al.

Computational Design approaches facilitate the generation of typographic design, but evaluating these designs remains a challenging task. In this paper, we propose a set of heuristic metrics for typographic design evaluation, focusing on their legibility, which assesses the text visibility, aesthetics, which evaluates the visual quality of the design, and semantic features, which estimate how effectively the design conveys the content semantics. We experiment with a constrained evolutionary approach for generating typographic posters, incorporating the proposed evaluation metrics with varied setups, and treating the legibility metrics as constraints. We also integrate emotion recognition to identify text semantics automatically and analyse the performance of the approach and the visual characteristics outputs.

J. J. Merelo, P. A. Castillo, Antonio M. Mora et al.

Introducing new technologies such as messaging platforms, and the chatbots attached to them, in higher education, is rapidly growing. This introduction entails a careful consideration of the potential opportunities and/or challenges of adopting these tools. Hence, a thorough examination of the teachers' experiences in this discipline can shed light on the effective ways of enhancing students' learning and boosting their progress. In this contribution, we have surveyed the opinions of tertiary education teachers based in Spain (mainly) and Spanish-speaking countries. The focus of these surveys is to collect teachers' feedback about their opinions regarding the introduction of the messaging platforms and chatbots in their classes, understand their needs and to gather information about the various educational use cases where these tools are valuable. In addition, an analysis of how and when teachers' opinions towards the use of these tools can vary across gender, experience, and their discipline of specialisation is presented. The key findings of this study highlight the factors that can contribute to the advancement of the adoption of messaging platforms and chatbots in higher education institutions to achieve the desired learning outcomes.

Jerry Swan, Steven Adriaensen, Alexander E. I. Brownlee et al.

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. We argue that, via principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.

J. J. Merelo, Nuria Rico, Israel Blancas et al.

Creating rankings might seem like a vain exercise in belly-button gazing, even more so for people so unlike that kind of things as programmers. However, in this paper we will try to prove how creating city (or province) based rankings in Spain has led to all kind of interesting effects, including increased productivity and community building. We describe the methodology we have used to search for programmers residing in a particular province focusing on those where most population is concentrated and apply different measures to show how these communities differ in structure, number and productivity.

R. H. García-Ortega, P. García-Sánchez, J. J. Merelo

The creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and plots to create an engaging narrative. This work presents a simulated environment with hundreds of characters that allows the study of coherent and interesting literary archetypes (or behaviours), plots and sub-plots. We will use this environment to perform a study about the number of profiles (parameters that define the personality of a character) needed to create two emergent scenes of archetypes: "natality control" and "revenge". A Genetic Algorithm (GA) will be used to find the fittest number of profiles and parameter configuration that enables the existence of the desired archetypes (played by the characters without their explicit knowledge). The results show that parametrizing this complex system is possible and that these kind of archetypes can emerge in the given environment.

J. J. Merelo, Antonio M. Mora, Carlos Cotta et al.

Mastermind is in essence a search problem in which a string of symbols that is kept secret must be found by sequentially playing strings that use the same alphabet, and using the responses that indicate how close are those other strings to the secret one as hints. Although it is commercialized as a game, it is a combinatorial problem of high complexity, with applications on fields that range from computer security to genomics. As such a kind of problem, there are no exact solutions; even exhaustive search methods rely on heuristics to choose, at every step, strings to get the best possible hint. These methods mostly try to play the move that offers the best reduction in search space size in the next step; this move is chosen according to an empirical score. However, in this paper we will examine several state of the art exhaustive search methods and show that another factor, the presence of the actual solution among the candidate moves, or, in other words, the fact that the actual solution has the highest score, plays also a very important role. Using that, we will propose new exhaustive search approaches that obtain results which are comparable to the classic ones, and besides, are better suited as a basis for non-exhaustive search strategies such as evolutionary algorithms, since their behavior in a series of key indicators is better than the classical algorithms.