Scaling Genetic Algorithms Using MapReduce

Genetic algorithms (GAs) are increasingly being applied to large scale problems. The traditional MPI-based parallel GAs require detailed knowledge about machine architecture. On the other hand, MapReduce is a powerful abstraction proposed by Google for making scalable and fault tolerant applications. In this paper, we show how genetic algorithms can be modeled into the MapReduce model. We describe the algorithm design and implementation of GAs on Hadoop, an open source implementation of MapReduce. Our experiments demonstrate the convergence and scalability up to 10⁵ variable problems. Adding more resources would enable us to solve even larger problems without any changes in the algorithms and implementation since we do not introduce any performance bottlenecks.