MILA

T-cell-dependent humoral immune response is one of the more complex immunological events in the biological immune system, involving interaction of B cells with antigen (Ag) and their proliferation, differentiation and subsequent secretion of antibody (Ab). Inspired by these immunological principles, a Multilevel Immune Learning Algorithm (MILA) is proposed for novel pattern recognition. This paper describes the detailed background of MILA, and outlines its main features in different phases: Initialization phase, Recognition phase, Evolutionary phase and Response phase. Different test problems are studied and experimented with MILA for performance evaluation. The results show MILA is flexible and efficient in detecting anomalies and novel patterns.


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Tien, Jia-Ping; Li, Tzuu-Hseng S.: Hybrid Taguchi-chaos of multilevel immune and the artificial bee colony algorithm for parameter identification of chaotic systems (2012)
  2. Bellucci, S.; Ohanyan, V.: Lattice distortions in a sawtooth chain with Heisenberg and Ising bonds (2010)
  3. Rosales, H. D.; Cabra, D. C.: Coexisting orders in the quarter-filled Hubbard chain with elastic deformations (2010)
  4. Farnell, D. J. J.; Richter, J.; Zinke, R.; Bishop, R. F.: High-order coupled cluster method (CCM) calculations for quantum magnets with valence-bond ground states (2009)
  5. Ji, Zhou; Dasgupta, Dipankar: V-detector: An efficient negative selection algorithm with “probably adequate” detector coverage (2009) ioport
  6. Golzari, Shahram; Doraisamy, Shyamala; Sulaiman, Md Nasir B.; Udzir, Nur Izura: A review on concepts, algorithms and recognition based applications of artificial immune system (2008)
  7. Kalz, A.; Honecker, A.; Fuchs, S.; Pruschke, T.: Phase diagram of the Ising square lattice with competing interactions (2008)
  8. Dasgupta, D.; Yu, S.; Majumdar, N. S.: MILA -- multilevel immune learning algorithm and its application to anomaly detection (2005)
  9. van Enter, Aernout C. D.; Shlosman, Senya B.: Provable first-order transitions for nonlinear vector and gauge models with continuous symmetries (2005)
  10. Dasgupta, Dipankar; Yu, Senhua; Majumdar, Nivedita Sumi: MILA -- multilevel immune learning algorithm (2003)