A Comparative Study of Genetic Algorithm and Particle Swarm optimisation for Dendritic Cell Algorithm

Nnko, Noe, Yang, Longzhi, Chao, Fei and Naik, Nitin (2020) A Comparative Study of Genetic Algorithm and Particle Swarm optimisation for Dendritic Cell Algorithm. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, Piscataway, NJ, pp. 1-8. ISBN 9781728169309, 9781728169293

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Official URL: https://doi.org/10.1109/cec48606.2020.9185497


Dendritic cell algorithm (DCA) is a class of artificial immune systems that was originally developed for anomaly detection in networked systems and later as a general binary classifier. Conventionally, in its life cycle, the DCA goes through four phases including feature categorisation into artificial signals, context detection of data items, context assignment, and finally labeling of data items as either abnormal or normal class. During the context detection phase, the DCA requires users to manually pre-define the parameters used by its weighted function to process the signals and data items. Notice that the manual derivation of the parameters of the DCA cannot guarantee the optimal set of weights being used, research attention has thus been attracted to the optimisation of the parameters. This paper reports a systematic comparative study between Genetic algorithm (GA) and Particle Swarm optimisation (PSO) on parameter optimisation for DCA. In order to evaluate the performance of GADCA and PSO-DCA, twelve publicly available datasets from UCI machine learning repository were employed. The performance results based on the computational time, classification accuracy, sensitivity, F-measure, and precision show that, the GA-DCA overall outperforms PSO-DCA for most of the datasets.

Item Type: Book Section
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
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Depositing User: Elena Carlaw
Date Deposited: 02 Nov 2020 12:21
Last Modified: 31 Jul 2021 13:18
URI: http://nrl.northumbria.ac.uk/id/eprint/44660

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