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.
|Title of host publication||2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings|
|Publication status||Published - 3 Sep 2020|
|Event||2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom|
Duration: 19 Jul 2020 → 24 Jul 2020
|Name||2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings|
|Conference||2020 IEEE Congress on Evolutionary Computation, CEC 2020|
|Period||19/07/20 → 24/07/20|
Bibliographical noteFunding Information:
This work has been supported by the Commonwealth Scholarship Commission (CSC-TZCS-2017-717) and Northumbria University, UK.
© 2020 IEEE.
Copyright 2020 Elsevier B.V., All rights reserved.
- artificial immune systems
- danger theory
- Dendritic cell algorithm
- genetic algorithm
- particles swarm optimisation