Counter-niching for constructive population diversity

Maintaining a desired level of diversity in the evolutionary algorithm (EA) population is a requirement to ensure that progress of the EA search is unhindered by premature convergence to suboptimal solutions. Loss of diversity in the EA population pushes the search to a state where the genetic operators can no longer produce superior or even different offspring required to escape the local optimum. Besides diversity´s contribution to avoid premature convergence, it is also useful to locate multiple optima where there is more than one solution available. This paper presents a counter-niching technique to introduce and maintain constructive diversity in the EA population. The proposed technique presented here uses informed genetic operations to reach promising, but un-explored or under-explored areas of the search space, while discouraging premature local convergence. Elitism is used at a different level aiming at convergence. The proposed technique´s improved performance in terms solution accuracy and computation time is observed through simulation runs on a number of standard benchmark test functions with a genetic algorithm (GA) implementation.