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

Noe Elisa Nnko, Longzhi Yang, Fei Chao, Nitin Naik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
3 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages8
ISBN (Electronic)9781728169293
ISBN (Print)9781728169309
DOIs
Publication statusPublished - Jul 2020
Event2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Conference

Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

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