Abstract
Verifying the published results of algorithms is part of the usual research process. This helps to both validate the existing literature, but also quite often allows for new insights and augmentations of current systems in a methodological manner. This is very pertinent in emerging new areas such as Artificial Immune Systems, where it is essential that any algorithm is well understood and investigated. The work presented in this paper results from an investigation into the opt-aiNET algorithm, a well-known immune inspired algorithm for function optimisation. Using the original source code developed for opt-aiNET, this paper identifies two minor errors within the code, propose a slight augmentation of the algorithm to automate the process of peak identification: all of which affect the performance of the algorithm. Results are presented for testing of the existing algorithm and in addition, for a slightly modified version, which takes into account some of the issues discovered during the investigations.
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References
De Castro, L.N., Von Zuben, F.: aiNET: An Artificial Immune Network for Data Analysis. In: Abbas, H., Sarker, R., Newton, C. (eds.) Data Mining: A Heuristic Approach, Idea Group Publishing, USA (2001)
De Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Function Optimisation. In: Proc. Of IEEE World Congress on Evolutionary Computation. pp. 669–674 (2002)
De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
Timmis, J., Neal, M.: A Resource Limited Artificial Immune System for Data Analysis. Knowledge Based Systems 14(3-4), 121–130 (2001)
Knight, T., Timmis, J.: AINE: An Immunological Approach to Data Mining. In: Cercone, N., Lin, T., Wu, X. (eds.) IEEE International Conference on Data Mining, San Jose. CA, pp. 297–304 (2001)
De Castro, L.N., Von Zuben, F.J.: Learning and Optimisation Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems 6(3), 239–251 (2002)
Jerne, N.: Towards a Network theory for the Immune System. Annals of Immunology, Inst. Pasture (1975)
(name removed for blind review) Artificial Immune Networks and Multimodal Optimisation. MSc Thesis (place removed for blind review)
Bezerra, B., De Castro, L.N.: Bioinformatics data analysis using an artificial immune network. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 22–33. Springer, Heidelberg (2003)
De Castro, L.N.: The Immune response of an Artificial Immune Network (aiNET). In: Congress on Evolutionary Computation (CEC), pp. 1273–1280. IEEE press, Los Alamitos (2003)
Walker, J., Garrett, G.: Dynamic Function Optimisation: Comparing the Performance of Clonal Selection and Evolutionary Strategies. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 273–284. Springer, Heidelberg (2003)
Kelsey, J., Timmis, J., Hone, A.: Chasing Chaos. In: Proceedings of the Congress on Evolutionary Computation (CEC), pp. 219–413. IEEE, Los Alamitos (2003)
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Timmis, J., Edmonds, C. (2004). A Comment on Opt-AiNET: An Immune Network Algorithm for Optimisation. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24854-5_32
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DOI: https://doi.org/10.1007/978-3-540-24854-5_32
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