11 research outputs found
A Genetic Programming infrastructure profiting from public computation resources
In this article an experience of the utilization of PRC (Public Resource Computation) in
research projects that needs large quantities of CPU time is presented. We have developed
a distributed architecture based on middleware BOINC and LilGP Genetic Programming
tool. In order to run LilGP applications under BOINC platforms, some core LilGP functions
has been adapted to BOINC requirements. We have used a classic GP problem known
as the artificial ANT in Santa Fe Trail. Some computers from a classroom were used acting
as clients, proving that they can be used for scientific computation in conjunction with their
primary uses
Finite group actions on reductive groups and buildings and tamely-ramified descent in Bruhat-Tits theory
The purpose of the paper is to give a new approach to tamely-ramified descent
in Bruhat-Tits theory. This descent was first studied by Guy Rousseau in his
thesis.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1611.0743
Una Herramienta de Programación Genética Paralela que Aprovecha Recursos Públicos de Computación
Éste artículo presenta una primera
implementación de una herramienta genérica de
programación genética capaz de aprovechar recursos
públicos de computación. Dadas las altas necesidades de
recursos de computación requeridos por los algoritmos
evolutivos, la aplicación del paralelismo ha sido habitual
recientemente, aunque las herramientas paralelas
requieren infraestructuras costosas para su
aprovechamiento. El modelo que se presenta en este
artículo, permite utilizar computadores distribuidos en
Internet, cuyos usuarios ceden altruistamente para
colaborar en proyectos de investigación. El proceso de
donación de recursos es simple e inmediato por parte del
usuario, afectando solamente a los ciclos de CPU que no
son consumidos por el propio usuario. Se estudia la
mejora de las prestaciones obtenidas gracias al uso de
estos recursos en Programación Genética Distribuida
Laser Dynamics Modelling and Simulation: An application of Dynamic Load Balancing of Parallel Cellular Automata
Characterizing Fault Tolerance in Genetic Programming
Evolutionary Algorithms, including Genetic Programming (GP), are frequently employed to solve difficult real-life problems, which can require up to days or months of computation. An approach for reducing the time-to-solution is to use parallel computing on distributed platforms. Large such platforms are prone to failures, which can even be commonplace events rather than rare occurrences. Thus, fault tolerance and recovery techniques are typically necessary. The aim of this article is to show the inherent ability of Parallel GP to tolerate failures in distributed platforms without using any fault-tolerant technique. This ability is quantified via simulation experiments performed using failure traces from real-world distributed platforms, namely, desktop grids, for two well-known problems