Chasing Chaos

Kelsey, Johnny and Timmis, Jon and Hone, Andrew N.W. (2003) Chasing Chaos. In: Sarker, Ruhul Amin and Reynolds, R. and Abbass, Hussein Aly and Kay-Chen, T. and McKay, R. and Essam, D. and Gedeon, T., eds. IEEE Congress on Evolutionary Computation. IEEE, Canberra. Australia pp. 413-419. ISBN 0-7803-7804-0 . (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Both simple and hybrid genetic algorithms encounter difficulties when presented with a function which has multiple values. Similarly, changing environments or functions which change rapidly present other problems. This paper presents an algorithm that is capable of coping with both of these scenarios: it can accommodate multiple solutions simultaneously and can track changes in optima efficiently. The proposed B-cell algorithm is inspired by the natural immune system, which itself displays similar capabilities of tracking multiple, moving targets in the form of infectious agents. This paper employs two nonlinear mappings which display chaotic behaviour to demonstrate the effectiveness of the B-cell algorithm in tracking multiple, moving targets. A number of experiments are conducted and results reported from the B-cell algorithm and standard hybrid genetic algorithm approaches. These results show the benefit of the B-cell algorithm approach when compared against these heuristic approaches.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Andrew N W Hone
Date Deposited: 24 Nov 2008 18:00
Last Modified: 17 Jul 2014 11:52
Resource URI: (The current URI for this page, for reference purposes)
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