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Gene Regulation via Bloom Filter

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Part of the Algorithms for Intelligent Systems book series (AIS)

Abstract

This paper examines the effectiveness of Bloom filters as a mechanism for regulating gene expression in an evolutionary algorithm. This expands on previous work that introduced non-coding genes as a mechanism to improve population diversity which in turn allows for better adaptation to changes in the environment. A prototype algorithm is developed and compared to the standard genetic algorithm and previously developed algorithms that made use of non-coding genes. Results show that the algorithm making use of non-coding genes managed by a Bloom filter is able to adapt to changes in the environment faster than a standard genetic algorithm, in some cases. The Bloom filter variation did, however, fall between the standard genetic algorithm and the previously developed variation based on using standard arrays to store gene-coding information.

Keywords

  • Evolutionary algorithm
  • Simulated epigenetics
  • Bloom filter
  • Dynamic environment
  • Population diversity
  • Probabilistic data structure

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  • DOI: 10.1007/978-981-16-6460-1_7
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Correspondence to Duncan A. Coulter .

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Cilliers, M., Coulter, D.A. (2022). Gene Regulation via Bloom Filter. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_7

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