Genetic Algorithms with Mapreduce Runtimes

Data-intensive Computing has played a key role in processing vast volumes of data exploiting massive parallelism. Parallel computing frameworks have proven that terabytes of data can be routinely processed. Mapreduce is a parallel programming model and associated implementation founded by Google, which is one of the leading companies in IT. Genetic Algorithms have increasingly applied on parallel computing to large scale problems. Since, GAs have the parallelism in their nature, they can be easily applied on Parallel Runtimes such as MPI, Hadoop Runtime and Twister[9]. Our researches have shown that, Genetic Algorithms were very successful and can be modeled on MPI (Message Passing Interface) and Mapreduce Models. We will explain the nature of Genetic Algorithms, how they are efficient to use Parallel computing networks and how can they be applied MapReduce Model. In this project, we will be applying Genetic Algorithms, primarily the basic algorithm Onemax Problem on Hadoop Mapreduce Framework and the Twister Iterative Mapreduce Framework and do the performance Analysis. Our expectation from this project, to prove that the GAs will perform better performance on Iterative Model from its nature and Twister’s computing model.