Diversity Handling In Evolutionary Landscape

Research output: Book chapter/Published conference paperConference paperpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2014)
Place of PublicationSwitzerland
Number of pages8
Publication statusPublished - 2014
EventInternational Workshop on Combinations of Intelligent Methods and Applications - Limassol, Cyprus
Duration: 10 Nov 201411 Nov 2014


WorkshopInternational Workshop on Combinations of Intelligent Methods and Applications


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