One of our most recent research lines deals with network topologies for the Particle Swarm Optimization algorithm (PSO). We have been studying dynamic and partially connected topologies and the first results are reported in two papers recently published in CEC and GECCO: Partially Connected Topologies for Particle Swarm (GECCO) and A Study on Time-Varying Partially Connected Topologies for the Particle Swarm (CEC). We have concluded that a random and dynamic partially connected grid topology with von Neumann neighborhood is able to perform more consistenly than tradicional configurations. This is the abstract of the paper published in the CEC proceedings:
This paper presents a study on the effects of dynamic and partially connected 2-dimensional topologies on the performance of the particle swarm optimization (PSO). The swarm is positioned on 2-dimensional grids of nodes and the particles move through the nodes according to a simple rule. Meanwhile, the von Neumann neighborhood is used to decide which particles influence each individual. Structures with growing size are tested on a classical benchmark and compared to several configurations such as lbest, gbest and the standard von Neumann configuration. The results show that the partially connected grids with von Neumann neighborhood structure performs more consistently when compared to lbest, gbest and the standard von Neumann topology.
In this poster we attempt to create some guidelines for finding solutions to the game of MasterMind using evolutionary algorithms. We fix the maximum size of the consistent set and use it throughout all problem sizes, although what we discover here is that set size is different for different scoring methods: Entropy or Most Parts. This paper continues the one presented at LION, where we studied only the size needed by Most Parts, which also extended the one presented at GAMEON.
The paper presented at GECCO, which was admitted as full paper, goes in a different direction and is called Improving evolutionary solutions to the game of MasterMind using an entropy-based scoring method. In this case just Entropy is used for scoring (which means that combinations that partition the set of consistent combinations with maximum entropy will be played and sought in the evolutionary search), and the objective was to try and find fast and efficient solutions so that bigger search spaces could be explored. It was pretty much achieved; fastest solution so far (but still slower, mainly due to the nature of Perl, than the solution by Berghman et al.) and best results that those published before by ourselves and others, so all in all we are quite happy about it. In this case I used impress.js for this awesome presentation (which took me quite a while indeed).
Inauguramos hoy una nueva sección en nuestra web bajo el título de “Courses/Cursos”.
En ella iremos enlazando a los distintos cursos que organizamos o en los que participamos. Como por ejemplo, el Curso de Diseño y Programación Web, que ya va por su 14ª edición, y que se desarrollará en granada del 23 de septiembre al 4 de octubre de 2013.
Yesterday I presented my work Migration Study on a Pareto-based Island Model for MOACOs, accepted as full-paper at the Genetic and Evolutionary Computation Conference 2013, held in Amsterdam.
The paper abstract is:
Pareto-based island model is a multi-colony distribution scheme recently presented for the resolution, by means of ant colony optimization algorithms, of bi-criteria problems. It yielded very promising results, but the model was implemented considering a unique Pareto-front-shaped unidirectional neighborhood migration topology, and a constant migration rate. In the present work two additional neighborhood topology schemes, and four different migration rates have been tested, considering the algorithm which obtained the best results in average in the model presentation article: MOACS (Multi-Objective Ant Colony System). Several experiments have been conducted, including statistical tests for better support the study. High values for the migration rate and the use of a bidirectional neighborhood migration topology yields the best results.
This week several members of Geneura group and ANYSELF project are attending to GECCO 2013 conference in Amsterdam. I’ve presented two papers related with OSGiLiath:
The first one, entitled Developing Services in a Service Oriented Architecture for Evolutionary Algorithms has been presented inside the EvoSoft workshop. It is a more technical continuation of the work “Service Oriented Evolutionary Algorithms“. Here is the abstract:
This paper shows the design and implementation of services for Evolutionary Computation in the Service Oriented Architecture paradigm. This paradigm allows independence in language and distribution, but the development requires to manage some technological and design issues, such as abstract design or unordered execution. To solve them, OSGiLiath, an implementation of an abstract Service Oriented Architecture for Evolutionary Algorithms, is used to develop new interoperable services taking into account these restrictions.
And here the presentation:
I also have presented the work “A Service Oriented…