An informed operator approach to tackle diversity constraints in evolutionary search

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. The proposed technique presented here, uses informed genetic operations to reach promising, but un/under-explored areas of the search space, while discouraging local 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.