Chris McEwan
On clonal selection
McEwan, Chris; Hart, Emma
Abstract
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.
Citation
McEwan, C., & Hart, E. (2011). On clonal selection. Theoretical Computer Science, 412(6), 502-516. https://doi.org/10.1016/j.tcs.2010.11.017
Journal Article Type | Article |
---|---|
Online Publication Date | Nov 19, 2010 |
Publication Date | 2011-02 |
Deposit Date | Jan 25, 2011 |
Journal | Theoretical Computer Science |
Print ISSN | 0304-3975 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 412 |
Issue | 6 |
Pages | 502-516 |
DOI | https://doi.org/10.1016/j.tcs.2010.11.017 |
Keywords | Clonal selection; optimisation;machine learning; EM algorithm |
Public URL | http://researchrepository.napier.ac.uk/id/eprint/4133 |
You might also like
Evolutionary Computation Combinatorial Optimization.
(2004)
Journal Article
A hyper-heuristic ensemble method for static job-shop scheduling.
(2016)
Journal Article
A research agenda for metaheuristic standardization.
(2015)
Presentation / Conference Contribution
A Lifelong Learning Hyper-heuristic Method for Bin Packing
(2015)
Journal Article