Recent advances in artificial immune systems: Models and applications
The immune system is a remarkable information processing and self learning system that offers inspiration to build artificial immune system (AIS). The field of AIS has obtained a significant degree of success as a branch of Computational Intelligence since it emerged in the 1990s. This paper surveys the major works in the AIS field, in particular, it explores up-to-date advances in applied AIS during the last few years. This survey has revealed that recent research is centered on four major AIS algorithms: (1) negative selection algorithms; (2) artificial immune networks; (3) clonal selection algorithms; (4) Danger Theory and dendritic cell algorithms. However, other aspects of the biological immune system are motivating computer scientists and engineers to develop new models and problem solving methods. Though an extensive amount of AIS applications has been developed, the success of these applications is still limited by the lack of any exemplars that really stand out as killer AIS applications. © 2010 Elsevier B.V. All rights reserved.
Applied Soft Computing Journal
Dasgupta, D., Yu, S., & Nino, F. (2011). Recent advances in artificial immune systems: Models and applications. Applied Soft Computing Journal, 11 (2), 1574-1587. https://doi.org/10.1016/j.asoc.2010.08.024