Investigating the evolution and stability of a resource limited artificial immune system

Timmis, Jon and Neal, Mark (2000) Investigating the evolution and stability of a resource limited artificial immune system. In: Special Workshop on Artificial Immune Systems, Gentic and Evolutionay Computtion Conference (GECCO) 2000. (Full text available)

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This paper presents a resource limited artificial immune system for data analysis. The work presented here builds upon previous work on artificial immune systems for data analysis. A population control mechanism, inspired by the natural immune system, has been introduced to control population growth and allow termination of the learning algorithm. The new algorithm is presented, along with the immunological metaphors used as inspiration. Results are presented for the Fisher Iris data set, where very successful results are obtained in identifying clusters within the data set. It is argued that this new resource based mechanism is a large step forward in making artificial immune systems a viable contender for effective unsupervised machine learning and allows for not just a one shot learning mechanism, but a continual learning model to be developed.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: artificial immune system, data analysis
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 09 Sep 2009 14:48 UTC
Last Modified: 17 Jan 2017 23:40 UTC
Resource URI: (The current URI for this page, for reference purposes)
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