<![CDATA[Current genetic algorithm require both management of huge amounts of data and heavy computation, fulfilling these requirements calls for simple ways to implement parallel computing. In this paper, serial genetic algorithm was designed to parallel GA; this technology appears to be particularly well adapted to this task. Here we introduce two related mechanism: elite reserve strategy and MPI. The first can increase the possible to get the optimal solution of the population, while the message passing interface MPI support is adding to form a new coarse-grain model of distributed parallel genetic algorithm. This new algorithm is tested by the classical and effective Knapsack problem, analysis shows that, the introduction of the parallel strategies can reduce the communication between different machines and the scheduling time of the heterogeneous system, thereby accelerate the traditional genetic algorithm search process, ultimately concluded that the parallel genetic algorithm is very promising and this framework could have a wide range of applications while maintaining good computational efficiency, scalability and ease of maintenance. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1566]]>
