Abstract
When constructing biologically inspired algorithms, important properties to consider are openness, diversity, interaction, structure and scale. In this paper, we focus on the property of diversity. Introducing diversity into biologically inspired paradigms is a key feature of their success. Within the field of Artificial Immune Systems, little attention has been paid to this issue. Typically, techniques of diversity introduction, such as simple random number generation, are employed with little or no consideration to the application area. Using function optimisation as a case study, we propose a simple immune inspired mutation operator that is tailored to the problem at hand. We incorporate this diversity operator into a well known immune inspired algorithm, aiNet. Through this approach, we show that it is possible to improve the search capability of aiNet on hard to locate optima. We further illustrate that by incorporating the same mutation operator into aiNet when applied to clustering, it is observed that performance is neither improved nor sacrificed.
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de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
Freitas, A.A., Timmis, J.: Revisiting the foundations of artificial immune systems: A problem-oriented perspective. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 229–241. Springer, Heidelberg (2003)
Stepney, S., Smith, R.E., Timmis, J.I., Tyrrell, A.M.: Towards a conceptual framework for artificial immune systems. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 53–64. Springer, Heidelberg (2004)
de Castro, L.N., Von Zuben, F.J.: An evolutionary immune network for data clustering. In: Proceeding of the IEEE Brazilian Symposium on Artificial Neural Networks, pp. 84–89 (2000)
Forrest, S., Perelson, A., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the IEEE Symposium on Research in Security and Privacy, pp. 202–212 (1994)
de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6, 239–251 (2002)
Janeway, C.A., Travers, P., Walport, M., Shlomchik, M.: Immunobiology: The Immune System in Health and Disease, 5th edn. Garland Publishing (2001)
Hart, E., Ross, P.: Studies on the implications of shape-space models for idiotypic networks. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004, vol. 3239, pp. 413–426. Springer, Heidelberg (2004)
de Castro, L.N., Timmis, J.: An artificial immune network for multimodal function optimization. In: 2002 Congress on Evolutionary Computation, pp. 699–704 (2002)
de Castro, L.N., Von Zuben, F.J.: ainet: An artificial immune network for data analysis. In: Abbass, H.A., Sarker, R.A., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, pp. 231–259. Idea Group Publishing (2002)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Timmis, J., Edmonds, C.: A comment on opt-ainet: An immune network algorithm for optimisation. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 308–317. Springer, Heidelberg (2004)
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Andrews, P.S., Timmis, J. (2006). On Diversity and Artificial Immune Systems: Incorporating a Diversity Operator into aiNet. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_37
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DOI: https://doi.org/10.1007/11731177_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
Online ISBN: 978-3-540-33184-1
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