Evolutionary Landscape and Management of Population Diversity
This work addresses a key issue in evolutionary computation for researchers and practitioners, but it appears incremental as it builds on existing counter-niching methods.
The paper tackles the problem of premature convergence in Evolutionary Algorithms by proposing a counter-niching technique to manage population diversity, showing promising results on standard benchmark test functions.
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.