Parallelization of genetic algorithms using Hadoop Map/Reduce

In this paper we present parallel implementation of genetic algorithm using map/reduce programming paradigm. Hadoop implementation of map/reduce library is used for this purpose. We compare our implementation with implementation presented in [1]. These two implementations are compared in solving One Max (Bit counting) problem. The comparison criteria between implementations are fitness convergence, quality of final solution, algorithm scalability, and cloud resource utilization. Our model for parallelization of genetic algorithm shows better performances and fitness convergence than model presented in [1], but our model has lower quality of solution because of species problem.