Metaheurísticas, optimización multiobjetivo y paralelismo para descubrir motifs en secuencias de ADN

La resolución de problemas complejos mediante técnicas evolutivas es uno de los aspectos más investigados en Informática. El objetivo principal de esta tesis doctoral es desarrollar nuevos algoritmos capaces de resolver estos problemas con el menor tiempo computacional posible, mejorando la calidad de los resultados obtenidos por los métodos ya existentes. Para ello, combinamos tres conceptos importantes: metaheurísticas, optimización multiobjetivo y paralelismo. Con este fin, primero buscamos un problema de optimización importante que aún no fuese resuelto de forma eficiente y encontramos el Problema del Descubrimiento de Motifs (PDM). El PDM tiene como objetivo descubrir pequeños patrones repetidos (motifs) en conjuntos de secuencias de ADN que puedan poseer cierto significado biológico. Para abordarlo, definimos una formulación multiobjetivo adecuada a los requerimientos del mundo real, implementamos un total de diez algoritmos de distinta naturaleza (población, trayectoria, inteligencia colectiva...), analizando aspectos como la capacidad de escalar y converger. Finalmente, diseñamos diversas técnicas paralelas, haciendo uso de entornos de programación como OpenMP y MPI, que tratan de combinar las propiedades de varias metaheurísticas en una única aplicación. Los resultados obtenidos son estudiados en detalle a través de la aplicación de numerosos test estadísticos, y las predicciones son comparadas con las descubiertas por un total de trece herramientas biológicas bien conocidas en la literatura. Las conclusiones obtenidas demuestran que la utilización de la optimización multiobjetivo en técnicas metaheurísticas favorece el descubrimiento de soluciones de calidad y que el paralelismo es útil para combinar las propiedades evolutivas de diferentes algoritmos.The resolution of complex problems by using evolutionary algorithms is one of the most researched issues in Computer Science. The main goal of this thesis is directly related with the development of new algorithms that can solve this kind of problems with the least possible computational time, improving the results achieved by the existing methods. To this end, we combine three important concepts: metaheuristics, multiobjective optimization, and parallelism. For doing this, we first look for a significant optimization problem that had not been solved in an efficient way and we find the Motif Discovery Problem (MDP). MDP aims to discover over-represented short patterns (motifs) in a set of DNA sequences that may have some biological significance. To address it, we defined a multiobjective formulation adjusted to the real-world biological requirements, we implemented a total of ten algorithms of different nature (population, trajectory, collective intelligence...), analyzing aspects such as the ability to scale and converge. Finally, we designed parallel techniques, by using parallel and distributed programming environments as OpenMP and MPI, which try to combine the properties of several metaheuristics in a single application. The obtained results are discussed in detail through numerous statistical tests, and the achieved predictions are compared with those discovered by a total of thirteen well-known biological tools. The drawn conclusions demonstrate that using multiobjective optimization in metaheuristic techniques favors the discovery of quality solutions, and that parallelism is useful for combining the properties of different evolutionary algorithms.Ministerio de Economía y Competitividad - FEDER (TIN2008-06491-C04-04; TIN2012-30685) Gobierno de Extremadura (GR10025-TIC015

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms

Сучасні інформаційні системи і технології

До збірника увійшли тези доповідей Першої міжнародної науково-практичної конференції «Сучасні інформаційні системи і технології, AIST 2012» (м.Суми, 15-18 травня 2012р). Матеріали, розміщені у збірнику, будуть корисні для студентів, аспірантів, науковців і фахівців галузі інформаційних технологій. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/2823

Сучасні інформаційні системи і технології

До збірника увійшли тези доповідей Першої міжнародної науково-практичної конференції «Сучасні інформаційні системи і технології, AIST 2012» (м.Суми, 15-18 травня 2012р). Матеріали, розміщені у збірнику, будуть корисні для студентів, аспірантів, науковців і фахівців галузі інформаційних технологій. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/2823

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance