Evolutionary landscape and management of population diversity

Research output: Book chapter/Published conference paperChapter (peer-reviewed)peer-review

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The search ability of an Evolutionary Algorithm (EA) depends on the variation among the individuals in the population [1â''3]. Maintaining an optimal level of diversity in the population is imperative to ensure that progress of the 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 population diversity issues. Next we present an investigation on a counter-niching EA technique [2] 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.
Original languageEnglish
Title of host publicationCombinations of intelligent methods and applications
Subtitle of host publicationProceedings of the 4th International Workshop, CIMA 2014, Limassol, Cyprus, November 2014 (at ICTAI 2014)
EditorsIoannis Hatzilygeroudis, Vasile Palade, Jim Prentzas
Place of PublicationSwitzerland
PublisherSpringer-Verlag London Ltd.
Number of pages18
ISBN (Electronic)9783319268606
ISBN (Print)9783319268583
Publication statusPublished - 2016
Event4th International Workshop on Combinations of Intelligent Methods and Applications : CIMA 2014 - Limassol, Cyprus
Duration: 10 Nov 201411 Nov 2014

Publication series

NameSmart Innovation, Systems and Technologies


Conference4th International Workshop on Combinations of Intelligent Methods and Applications


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