A Grid-Oriented Genetic Algorithm

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 GridW ay framework. The efficiency and reliability of this schema to solve the One Max problem is analyzed in a globus-based research testbed.

• Type: Conference
• Year: 2005
• Where: EGC
• Authors: Jose Herrera, Eduardo Huedo, Rubén S. Montero, Ignacio Martín Llorente
• Keywords: Distributed And Parallel Computing, EGC 2005, Genetic Algorithms, Grid, Parallel Genetic Algorithms