Optimization problem solving framework employing GAs with linkage identification over a grid environment

This paper is a step towards a general purpose optimization problem-solving framework that can solve a large number of global optimization problems on its own with a minimal input from the user. It relies on competent GAs (genetic algorithms) as the solver and depends on Grid computing for the required computational resources. In this paper we will discuss the architecture of the framework in detail. In the results section we will discuss the speedups obtained by using parallel GAs over a Grid computing environment and the effects of Grid overheads on the speedup. Even though there are various advantages of using Grids but in the results section we will focus on the reduction in total execution time due to parallelism.