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Improving Population Diversity Through Gene Methylation Simulation

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11508)


During the runtime of many evolutionary algorithms, the diversity of the population starts out high and then rapidly diminishes as the algorithm converges. The diversity will directly influence the algorithm’s ability to perform effective exploration of the problem space. In most cases if exploration is required in the latter stages of the algorithm, there may be insufficient diversity to allow for this. This paper proposes an algorithm that will better maintain diversity throughout the runtime of the algorithm which will in turn allow for better exploration during the latter portion of the algorithm’s run.


  • Evolutionary algorithms
  • Population diversity
  • Exploration

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Correspondence to Duncan A. Coulter .

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Cilliers, M., Coulter, D.A. (2019). Improving Population Diversity Through Gene Methylation Simulation. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20911-7

  • Online ISBN: 978-3-030-20912-4

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