MRPGA: An Extension of MapReduce for Parallelizing Genetic Algorithms

The MapReduce programming model allows users to easily develop distributed applications in data centers. However, many applications cannot be exactly expressed with MapReduce due to their specific characteristics. For instance, genetic algorithms (GAs) naturally fit into an iterative style. That does not follow the two phase pattern of MapReduce. This paper presents an extension to the MapReduce model featuring a hierarchical reduction phase. This model is called MRPGA (MapReduce for parallel GAs), which can automatically parallelize GAs. We describe the design and implementation of the extended MapReduce model on a .NET-based enterprise grid system in detail. The evaluation of this model with its runtime system is presented using example applications.