  The search ability of an Evolutionary Algorithm (EA) depends on the variation
among the individuals in the population [3, 4, 8]. 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 brief
analysis of the EA population diversity issues. Next we present an
investigation on a counter-niching EA technique [4] that introduces and
maintains constructive diversity in the population. The proposed approach uses
informed genetic operations to reach promising, but unexplored or
under-explored areas of the search space, while discouraging premature local
convergence. Simulation runs on a suite of standard benchmark test functions
with Genetic Algorithm (GA) implementation shows promising results.
