About Designing an Observer Pattern-Based Architecture for a Multi-objective Metaheuristic Optimization Framework

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a user intends to add new features to the included algorithms. In this paper, we explore a different approach by designing a component-based architecture for a multi-objective optimization framework based on the observer pattern. In this architecture, most of the algorithmic components are observable entities that naturally allows to register a number of observers. This way, a metaheuristic is composed of a set of observable and observer elements, which can be easily extended without requiring to modify the algorithm. We have developed a prototype of this architecture and implemented the NSGA-II evolutionary algorithm on top of it as a case study. Our analysis confirms the improvement of flexibility using this architecture, pointing out the requirements it imposes and how performance is affected when adopting it.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Voluntary task switching under load: contribution of top-down and bottom-up factors in goal-directed behavior

The present study investigated the relative contribution of bottom-up and top-down control to task selection in the voluntary task-switching (VTS) procedure. In order to manipulate the efficiency of top-down control, a concurrent working memory load was imposed during VTS. In three experiments, bottom-up factors, such as stimulus repetitions, repetition of irrelevant information, and stimulus task associations, were introduced in order to investigate their influence on task selection. We observed that the tendency to repeat tasks was stronger under load, suggesting that top-down control counteracts the automatic tendency to repeat tasks. The results also indicated that task selection can be guided by several elements in the environment, but that only the influence of stimulus repetitions depends on the efficiency of top-down control. The theoretical implications of these findings are discussed within the interplay between top-down and bottom-up control that underlies the voluntary selection of tasks

Ressources cognitives et développement territorial : une analyse textuelle appliquée aux politiques locales de développement durable

International audienceThis paper focuses on how the development of a particular region consistently requires actors to share a certain level of cognitive resources. In the case studied here - the Nord Pas-de-Calais region, this cognitive proximity is built via the local policies involving sustainable development. In order to comprehend the way local communities of the region activate this resource, we used textual data treatment analyzing about thirty interviews. The results suggest that this cognitive proximity relies on two fundamentals elements: on the one hand, valuing the patrimonial infrastructures of the territory; and on the other hand, rebuilding the territorial identity of the region. Then the local policies lean both on the values that underlie these elements and on rhetorical modalities, to impulse in-depth changes that would have been more difficult to implement from the usual political levers.Cet article étudie comment un développement régional cohérent nécessite la mobilisation de ressources cognitives partagées. Dans le cas étudié - la région Nord Pas-de-Calais, cette proximité cognitive se construit via les politiques locales, sur la base du référentiel de développement durable. Pour saisir la manière dont les collectivités de la région activent cette ressource, nous avons mobilisé les outils d'analyse textuelle sur une trentaine d'entretiens auprès des acteurs publics du développement durabe. Les résultats mis en évidence suggèrent que cette proximité cognitive repose sur deux éléments fondamentaux : d'une part la mise en valeur d'un patrimoine infrastructurel territorialisé, et d'autre part la reconstruction d'une identité territoriale. Les politiques locales prennent ainsi appui sur les valeurs qui sous-tendent ces éléments ainsi que sur des modalités rhétoriques, pour impulser en profondeur une dynamique de changement, plus malaisée à mettre en œuvre partir des outils politiques habituels

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components

No Evidence for Shared Representations of Task Sets in Joint Task Switching

Action Contro

Using AI Methods for Health Behavior Change

Artificial intelligence (AI) has been applied to health behavior change research for over a decade. Current research programs include machine learning for delivering just-in-time adaptive interventions, computational modeling of behavior change processes, and the use of social AI for communication and persuasion. With new advances in AI, we propose an international workshop to bring together experts from all related disciplines to discuss and explore the potentials of AI for behavior change research. We discuss in this proposal the aims, planned activities, expected outcomes, and a promotion strategy for the workshop

Efficient and accurate P-value computation for Position Weight Matrices

<p>Abstract</p> <p>Background</p> <p>Position Weight Matrices (PWMs) are probabilistic representations of signals in sequences. They are widely used to model approximate patterns in DNA or in protein sequences. The usage of PWMs needs as a prerequisite to knowing the statistical significance of a word according to its score. This is done by defining the P-value of a score, which is the probability that the background model can achieve a score larger than or equal to the observed value. This gives rise to the following problem: Given a P-value, find the corresponding score threshold. Existing methods rely on dynamic programming or probability generating functions. For many examples of PWMs, they fail to give accurate results in a reasonable amount of time.</p> <p>Results</p> <p>The contribution of this paper is two fold. First, we study the theoretical complexity of the problem, and we prove that it is NP-hard. Then, we describe a novel algorithm that solves the P-value problem efficiently. The main idea is to use a series of discretized score distributions that improves the final result step by step until some convergence criterion is met. Moreover, the algorithm is capable of calculating the exact P-value without any error, even for matrices with non-integer coefficient values. The same approach is also used to devise an accurate algorithm for the reverse problem: finding the P-value for a given score. Both methods are implemented in a software called TFM-PVALUE, that is freely available.</p> <p>Conclusion</p> <p>We have tested TFM-PVALUE on a large set of PWMs representing transcription factor binding sites. Experimental results show that it achieves better performance in terms of computational time and precision than existing tools.</p