However; as the evolutionary search progresses; it is important to avoid reaching a state where the genetic operators can no longer produce superior offspring; prematurely. This is likely to occur when the search space reaches a homogeneous or near-homogeneous configuration converging to a local optimal solution. Maintaining a certain degree of population diversity is widely believed to help curb this problem. This paper discusses the problem of premature convergence related to EA based optimization. A novel technique is presented; that uses informed genetic operations to reach promising; but un/under-explored areas of the search space; while discouraging local convergence; to curb premature convergence. Elitism is used at a different level aiming at convergence. The proposed technique’s improved performance in terms solution precision and convergence characteristics is observed on a number of benchmark test functions with a genetic algorithm (GA) implementation.