On Clonal Selection

McEwan, C., Hart, E. (2011). On Clonal Selection. Theoretical Computer Science, 412, (6), 502-516.


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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.
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Authors

Emma Hart
Director of CEC
e.hart@napier.ac.uk
+44 131 455 2783

Areas of Expertise

Data Intensive Systems
Data and information are key assets for modern business. Large complex and incomplete datasets are common in industry. Exploiting that data successfully can give a major competitive advantage while, if it is not managed successfully, its value is often lost.
Optimisation
In our increasingly global economy, companies are under pressure to optimise their activities in order to gain a competitive edge or to even remain sustainable. our optimisation research specialises in developing state-of-the-art algorithms that are capable of obtaining solutions to complex...

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