Research Output

On Clonal Selection.

  Clonal selection has been a dominant theme in many immune-inspired algorithms applied to machine learning and optimisation. We examine existing clonal selections algorithms for learning from a theoertical and empirical perspective and assert that the widely accepted computational interpretation of clonal selection is compromised both algorithmically andbiologically. We suggest a more capable abstraction of the clonal selection principle grounded in probabilistic estimation and approximation and demonstrate how it addresses some of the shortcomings in existing algorithms. We further show that by recasting black-box optimisation as a learning problem, the same abstraction may be re-employed; thereby taking steps toward unifying the clonal selection principle and distinguishing it from natural selection.

  • Type:

    Article

  • Date:

    31 January 2011

  • Publication Status:

    Published

  • Publisher

    Elsevier

  • DOI:

    10.1016/j.tcs.2010.11.017

  • ISSN:

    0304-3975

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

Citation

McEwan, C. & Hart, E. (2011). On Clonal Selection. Theoretical Computer Science. 412, 502-516. doi:10.1016/j.tcs.2010.11.017. ISSN 0304-3975

Authors

Keywords

Clonal selection; optimisation;machine learning; EM algorithm;

Available Documents