Memetic and Opposition-Based Learning Genetic Algorithms for Sorting Unsigned Genomes by Translocations

  • Lucas A. da Silveira
  • José L. Soncco-Álvarez
  • Thaynara A. de Lima
  • Mauricio Ayala-Rincón
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 419)

Abstract

A standard genetic algorithm (\(\mathcal{G\!A}_{{ \mathrm{S}}}\)) for sorting unsigned genomes by translocations is improved in two different manners: firstly, a memetic algorithm (\(\mathcal{G\!A}_{{ \mathrm{M}}}\)) is provided, which embeds a new stage of local search, based on the concept of mutation applied in only one gene; secondly, an opposition-based learning (\(\mathcal{G\!A}_{{ \mathrm{OBL}}}\)) mechanism is provided that explores the concept of internal opposition applied to a chromosome. Both approaches include a convergence control mechanism of the population using the Shannon entropy. For the experiments, both biological and synthetic genomes were used. The results showed that \(\mathcal{G\!A}_{{ \mathrm{M}}}\)outperforms both \(\mathcal{G\!A}_{{ \mathrm{S}}}\)and \(\mathcal{G\!A}_{{ \mathrm{OBL}}}\)as confirmed through statistical tests.

Keywords

Sorting permutations Sorting unsigned genomes Genetic algorithms Memetic algorithms Opposition-based learning algorithms 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Lucas A. da Silveira
    • 1
  • José L. Soncco-Álvarez
    • 1
  • Thaynara A. de Lima
    • 3
  • Mauricio Ayala-Rincón
    • 1
    • 2
  1. 1.Departaments of Computer ScienceUniversidade de BrasíliaBrasíliaBrazil
  2. 2.Departaments of Computer Science and MathematicsUniversidade de BrasíliaBrasíliaBrazil
  3. 3.Department of MathematicsUniversidade de Goiás—Campus IIGoiásBrazil

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