Abstract
Genetic algorithms (GAs) are stochastic search methods that have been successfully applied in many search, optimization, and machine learning problems. Their parallel counterpart (PGA, parallel genetic algorithms) offers many advantages over the traditional GAs, such as speed, ability to search on a larger search space, and less likely to run into a local optimum. With the advent of Grid computing, the computational power that can be deliver to the applications have substantially increased, and so PGAs can potentially benefit from this new Grid technologies. However, because of the dynamic and heterogeneous nature of Grid environments, the implementation and execution of PGAs in a Grid involve challenging issues. This paper discusses the distribution of a PGA across the Grid using the DRMAA standard API and the Grid Way framework. The efficiency and reliability of this schema to solve the One Max problem is analyzed in a globus-based research testbed.
This research was supported by Ministerio de Ciencia y Tecnología through the research grant TIC 2003-01321 and Instituto Nacional de Técnica Aeroespacial.
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© 2005 Springer-Verlag Berlin Heidelberg
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Herrera, J., Huedo, E., Montero, R.S., Llorente, I.M. (2005). A Grid-Oriented Genetic Algorithm. In: Sloot, P.M.A., Hoekstra, A.G., Priol, T., Reinefeld, A., Bubak, M. (eds) Advances in Grid Computing - EGC 2005. EGC 2005. Lecture Notes in Computer Science, vol 3470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508380_33
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DOI: https://doi.org/10.1007/11508380_33
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