INSPIRING FUTURES

On Clonal Selection.

McEwan, Chris and Hart, Emma (2011) On Clonal Selection. Theoretical Computer Science, 412 (6). pp. 502-516. ISSN 0304-3975

Full text not available from this repository. (Request a copy)

Abstract/Description

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.

Item Type: Article
Print ISSN: 0304-3975
Uncontrolled Keywords: Clonal selection; optimisation;machine learning; EM algorithm;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods > 006.3 Artificial intelligence
Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 4133
Depositing User: Computing Research
Date Deposited: 25 Jan 2011 16:50
Last Modified: 29 Nov 2012 10:56
URI: http://researchrepository.napier.ac.uk/id/eprint/4133

Actions (login required)

View Item

Edinburgh Napier University is a registered Scottish charity. Registration number SC018373