[Paper] Studying the effect of population size in distributed evolutionary algorithms on heterogeneous clusters

Finally we have published in Applied Soft Computer journal the last paper related with my thesis. We used OSGiLiath to perform a number of cool experiments related with automatic adaptation of dEAs on heterogeneous clusters.

The abstract:

Distributed Evolutionary Algorithms are traditionally executed on homogeneous dedicated clusters, despite most scientists have access mainly to networks of heterogeneous nodes (for example, desktop PCs in a lab). Fitting this kind of algorithms to these environments, so that they can take advantage of their heterogeneity to save running time, is still an open problem. The different computational power of the nodes affects the performance of the algorithm, and tuning or fitting it to each node properly could reduce execution time.

Since the distributed Evolutionary Algorithms include a whole range of parameters that influence the performance, this paper proposes a study on the population size. This parameter is one of the most important, since it has a direct relationship with the number of iterations needed to find the solution, as it affects the exploration factor of the algorithm. The aim of this paper consists in validating the following hypothesis: fitting the sub-population size to the computational power of the heterogeneous cluster node can lead to an improvement in running time with respect to the use of the same population size in every node.

Two parameter size schemes have been tested, an offline and an online parameter setting, and three problems with different characteristics and computational demands have been used.

Results show that setting the population size according to the computational power of each node in the heterogeneous cluster improves the time required to obtain the optimal solution. Meanwhile, the same set of different size values could not improve the running time to reach the optimum in a homogeneous cluster with respect to the same size in all nodes, indicating that the improvement is due to the interaction of the different hardware resources with the algorithm. In addition, a study on the influence of the different population sizes on each stage of the algorithm is presented. This opens a new research line on the fitting (offline or online) of parameters of the distributed Evolutionary Algorithms to the computational power of the devices.

The paper (currently «In press» form) can be downloaded from http://www.sciencedirect.com/science/article/pii/S1568494615006468

Y ya puestos… también hemos creado el beerjs de Granada

Tras la buena acogida del de Jaén, nos hemos animado también a crear el de Granada (https://github.com/beerjs/granada).

Aún estamos organizando un poco cómo irá la cosa, pero parece que podremos tener un huequito en la Oficina de Software Libre de la UGR.

En breve, empezamos a organizar eventos.

Hemos creado el grupo beerjs de Jaén

Después de algún tiempo de gestiones, hemos creado finalmente el grupo beerjs para Jaén.

Si eres de, vives en, pasas por o vas a Jaén y te gusta Javascript, no dudes en unirte a nosotros.

Echa un vistazo en https://github.com/beerjs/jaen y entérate de los eventos que organizamos.

Aplicando algoritmos genéticos a un controlador ‘fuzzy’ para una gestión adaptativa del tráfico

Recientemente se ha aceptado el artículo «A hybrid Fuzzy Genetic Algorithm for an adaptive traffic signal system» en la revista open-access Advances in Fuzzy Systems. En él participamos varios de los investigadores de GeNeura.

El abstract es:

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

Esperamos que os guste (y que lo citéis, claro :D).

Professor Alexander Lyubchenko introduces his research work to GeNeura

Professor Alexander Lyubchenko will be working with GeNeura Group for the next 10 months. Today, he has been showing us his main research lines.

GeNeura en la Noche de los Investigadores 2015

GeNeura estuvo presente dentro de la Noche Europea de los Investigadores en el stand de la actividad El Ojo que todo lo ve. En este evento estuvimos realizando difusión de los proyectos SIPESCA, MOSOS, PETRA y el recientemente solicitado SiMoPE. Dentro del stand estuvimos mostrando el estado de los proyectos, sus avances, los dispositivos de captación, y los datos de dispositivos móviles que habíamos ido tomando. Tuvo una gran afluencia de público, que participó de forma activa a través de preguntas y opiniones. Además, estuvimos midiendo el paso de las personas que estuvieron asistiendo al evento con uno de nuestros dispositivos.

Algunas fotos del evento:

Muchas gracias a todos los asistentes!