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

SwissTPH/app-rural-geolocator: MMP stable

No description provided

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

Abstract

computation resource