MoHPBGA: Multiobjective Hierarchical Population Balanced Genetic Algorithm using MapReduce

Use of heuristic methods is common to find the solutions to the optimization problems for scientific and real time. Problems such as Travelling Salesman (TSP) require more accurate solution which is tried by various optimization methods. Research in this direction shows the use of Genetic algorithms (GA) as promising candidate and is preferred over other optimization methods. Firstly due to the use of large population and secondly large number of iterations GA tends to be more accurate but inefficient with respect to computation time. Variants of GA are formulated and experimented so as to take care of execution time. We present the review of approaches used to formulation of GA solutions mainly parallel GA (PGA), distributed GA (DGA) and hierarchical parallel GA (HPGA). Further this paper proposes Multi objective Hierarchical Population Balanced Genetic Algorithm (MoHPBGA) as the improved candidate which uses map reduce framework for efficient use of population mapping and synchronization of tasks.