A review of the immunological inspired distributed learning environment

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/25553

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Title: A review of the immunological inspired distributed learning environment
Author(s): Brownlee, Jason
Abstract: The IIDLE is a machine-learning framework inspired by the information processing properties of clonal selection in the context of a spatially distributed and recirculation population of lymphocyte in a host organism. The IIDLE may be considered to have been proposed in the context ‘systems engineering’, with a strong top-down and application centric perspective. This work considers the IIDLE in the context of the previously hierarchical framework of the acquired immune system and related models and algorithms. The main information processing themes of the IIDLE are considered and broader integration of IIDLE and the hierarchal framework is considered.
Publication type: Technical report
Research centre: Swinburne University of Technology. Faculty of Information and Communication Technologies. Centre for Information Technology Research
Source: Complex Intelligent Systems : technical reports
Publication year: 2007
Keyword(s): Adaptive; AIS; Algorithm; Artificial immune system; Clonal selection; Distributed; Framework; IIDLE; Information processing; Integration
Publisher: Swinburne University of Technology
Publisher URL: http://www.ict.swin.edu.au/personal/jbrownlee/#2007
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