Abstract
Clonal Selection and Immune Network Theory are commonly adopted for resolving optimization problems. Here, the mechanisms of migration and maturation of Dendritic Cells (DCs) is adopted for pursuing pareto optimal solution( s) in complex problems, specifically, the adoption of multiple characters of distinct clones of DCs and the immunological control parameters in the process of signal cascading. Such an unconventional approach, namely, DC-mediated Signal Cascading Framework further exploits the intrinsic abilities of DCs, with the added benefit of overcoming some of the limitations of conventional optimization algorithms, such as convergence of the Pareto Front.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Oates, R., Kendall, G. & Garibaldi, J.M., Classifying in the presence of Uncertainty: A DCA Perspective, ICARIS 2010, pp. 75-87 (2010)
Oates, R., Greensmith, J., Aickelin, U., Garibaldi, J. & Kemdall, G., The Application of a Dendritic Cell Algorithm to a Robotic Classifier, ICARIS 2007, pp.204-215 (2007)
Greensmith, J., Aickelin, U. & Tedesco, G., Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm, Information Fusion, 11, pp.21-34 (2010)
Fanelli, R., Further Experimentations with Hybrid Immune Inspired Network Intrusion Detection, ICARIS 2010, pp. 264-275 (2010)
Greensmith, J., Aickelin, U. & Cayer, S., Introducing dendritic Cells as a novel immune- inspired algorithm for anomaly detection, ICARIS 2005, pp. 153-167 (2005)
Greensmith, J., Aickelin, U. & Tedesco, G., Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm, Information Fusion, 11, pp.21-34 (2010)
Martin-Fontecha, A., Lanzavecchia, A. & Sallusto, F., Dendritic Cell Migration to Peripheral Lymph Nodes, In: Lombardi, G. & Riffo-Vasquez (eds.) Dendritic Cells, pp.31- 49, Springer, Heidelberg (2009)
Ricart, B.G., John, G., Lee, D., Hunter, C.A. & Hammer, D.A., Dendritic Cells Distinguish Individual Chemokine Signals through CCR7 and CXCR4, J Immunol, 186, pp.53-61
Coello Coello, C. A., & Cruz Cortés, N., An Approach to Solve Multiobjective Optimization Problems Based on an Artificial Immune System., ICARIS 2002, pp. 212--221 (2002).
Freschi, R., Coello, C.A.C. & Repetto, M., Multiobjective optimization and artificial immune system: a review, retrieved at: http://www.igi-global.com/viewtitlesample.aspx?id=19637 (IGI Global), (2009).
Kim, D.H., Abraham, A. & Cho, J.H., A hybid genetic algorithm and bacterial foraging approach for global optimization, Information Sciences, 177(18), pp.3918-3937 (2007).
Sozzani, S., Allavena, P. &. & Mantovani, A., Dendritic cells and chemokines, In: Lotze, M.T. & Thomas, A.W. (eds.), Dendritic Cells, pp.203-211, Elseiver Academic Press, London (2001).
Sanchez-Sanches, N., Riol-Blanco, L. & Rodriguez-Fernandez, L., The multiple personalities of the chemokine receptor CCR7 in dendritic cells, J. Immunol., 176, pp.5153-5159 (2006).
Randolph, G.J., Ochando, J. & Parida-Sanchex, S., Migration of dendritic cell subsets and their precursors, Annu. Rev. Immunol., 26, pp.293-316 (2008)
Hochrein, H. & O’Keeffe, M., Dendritic cell subsets and toll-like receptors, In: Bauer, S. & Hartmann, G. (eds.), Toll-like Receptors (TLRs) and Innate Immunity – Handbook of Experimental Pharmacology, pp.153-179 (2008)
Van Hasster, P.J. & Devreptes, P.N., Chemotaxis: signaling the way forward, Nature Reviews Molecular Cell Biology, 5, pp. 626-634 (2004).
Shi, G.K., Harrison, K., Han, D.B., Moratz, C. & Kehrl, J.H., Toll-like receptor signaling alters the expression of regulator of G protein signaling protein in dendritic cells: implications for G protein-coupled receptor signaling, J. Immunol., 172(9), pp.5175-5144 (2004)
Blander, J. M. & Sander, L.E., Beyond pattern recognition: five immune checkpoints for scaling the microbial threat, Nature Reviews Immunology, 12, pp. 215-225 (2012).
Murphy, K.M. & Stockinger, B., Effector T-cell plasticity: flexibility in the face of changing circumstances, Nature Immunology, 11(8), pp.674-680 (2010).
Goodnow, C.C., Vinuesa, C.G., Randall, K.L., Mackay, F. & Brink, R., Control systems and decision making for antibody production, Nature Immunology, 11(8), pp.681-688 (2010).
Pulendran, B., Tang, H. & Manicassamy, S., Programming dendritic cells to induce TH2 and tolerogenic responses, Nature Immunology, 11(8), pp.647-655 (2010).
Ko, A., Lau, H.YK. & Lau, T.L., General Suppression Control Framework: Application in Self-balancing Robots, ICARIS 2005, pp.375-388 (2005)
Hart, E., Bersini, H. & Santos, F., Tolerance vs Intolerance: How Affinity Defines Topology in an Idiotpypic Network, ICARIS 2006, pp. 109-121 (2006)
Timmis, J., Neal, M. & Hunt, J., An artificial immune system for data analysis, Biosystems, 55(3), pp. 143-150 (2000)
Coello Coello, C.A. & Cortés, N.C., Solving multiobjective problems using an artificial immune system, Genetic Programming Evolvable Machines, 6, pp. 163-190 (2005).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London
About this paper
Cite this paper
Lee, N.M.Y., Lau, H.Y.K. (2012). A Cooperative Multi-objective Optimization Framework based on Dendritic Cells Migration Dynamics. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_15
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4739-8_15
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4738-1
Online ISBN: 978-1-4471-4739-8
eBook Packages: Computer ScienceComputer Science (R0)