  The search ability of an Evolutionary Algorithm (EA) depends on the variation
among the individuals in the population. Maintaining an optimal level of
diversity in the EA population is imperative to ensure that progress of the EA
search is unhindered by premature convergence to suboptimal solutions. Clearer
understanding of the concept of population diversity, in the context of
evolutionary search and premature convergence in particular, is the key to
designing efficient EAs. To this end, this paper first presents a comprehensive
analysis of the EA population diversity issues. Next we present an
investigation on a counter-niching EA technique that introduces and maintains
constructive diversity in the population. The proposed approach uses informed
genetic operations to reach promising, but un-explored or under-explored areas
of the search space, while discouraging premature local convergence. Simulation
runs on a number of standard benchmark test functions with Genetic Algorithm
(GA) implementation shows promising results.
