Abstract
Nature has often provided the inspiration needed for new computational paradigms and metaphors [1,16]. However natural systems do not exist in isolation and so it is only natural that hybrid approaches be explored. The article examines the interplay between three biologically inspired techniques derived from a plethora of natural phenomena. Cellular automata with their origins in crystalline lattice formation are coupled with the immune system derived clonal selection principle in order to regulate the convergence of the stochastic diffusion search algorithm. Stochastic diffusion search is itself biologically inspired in that it is an inherently multi-agent oriented search algorithm derived from the non-stigmergic tandem calling / running recruitment behaviour of ant species such as Temnothorax albipennis. The paper presents an invesitigation into the role cellular automata of differing complexity classes can play in order to establish a balancing mechanism between exploitation and exploration in the emergent behaviour of the system...
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References
Michalek, R., Tarantello, G.: Design Patterns from Biology for Distributed Computing. ACM Transactions on Autonomous and Adaptive Systems 1, 26–66 (2006)
Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE. John Wiley & Sons Ltd., Chichester (2007)
Coulter, D., Ehlers, E.: Federated Patent Harmonisation: An evolvable agent-wise approach. In: Proceedings of TMCE Symposium, vol. 2, pp. 1391–1393 (2008)
de Castro, L., Timmis, J.: Artificial Immune Systems A New Computational Intelligence Approach. Springer, Heidelberg (2002)
FIPA contributors FIPA ACL Message Structure Specificatio (2002), http://www.fipa.org/specs/fipa00061/CitedAugust2009
Franks, N.R., Richardson, T.: Teaching in tandem-running ants. Nature 493, 153 (2006)
Gong, M., Jiao, L., Ma, W.: Large-scale Optimization Using Immune Algorithm. In: GEC 2009, vol. 2, pp. 1391–1393 (2009)
Hilaire, V., Koukam, A., Rodriguez, S.: An Adaptative Agent Architecture for Holonic Multi-Agent Systems. ACM Transactions on Autonomous and Adaptive System (2008) doi: 10.1145/1342171.1342173
Hurley, S., Whitaker, R.M.: An Agent Based Approach to Site Selection for Wireless Networks. In: Proceedings of the 2002 ACM symposium on Applied computing (2002) doi: 10.1145/508791.508902
Hong, L.: On the Convergence Rates of Clonal Selection Algorithm. Information Science and Engieering (2008) doi: 10.1109/ISISE.2008.63
Karakasis, V.K., Stafylopatis, A.: Efficient Evolution of Accurate Classification Rules Using a Combination of Gene Expression Programming and Clonal Selection. IEEE Transactions on Evolutionary Computation (2008) doi: 10.1109/TEVC.2008.920673
De Meyer, K., Bishop, J.M., Nasuto, S.J.: Small-World Effects in Lattice Stochastic Diffusion Search. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 147–152. Springer, Heidelberg (2002)
De Meyer, K., Nasuto, S.J., Bishop, M.: Stochastic Diffusion Search: partial function evaluation in swarm intelligence dynamic optimisation. In: Abraham, A., Grosam, C., Ramos, V. (eds.) Swarm intelligence and data mining, ch. 2, vol. 2 (2006)
Odersky, M., Spoon, L., Venners, B.: Programming in Scala. Artima Press (2008)
OReilly, G.B., Ehlers, E.M.: The Artificial Collective Engine Utilising Stigmergy (ACEUS) a Framework for Building Adaptive Software Systems. IJCSNS International Journal of Computer Science and Network Security (2008) doi: 10.1.1.104.3513
Shen, H., Zhu, Y., Zhou, X., Guo, H., Chang, C.: Bacterial Foraging Optimization Algorithm with Particle Swarm Optimization Strategy for Global Numerical Optimization. In: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation (2009) doi: 10.1145/1543834.1543901
Wolfram, S.: A New Kind of Science. Wolfram Media, Inc. (2002)
In-Sob, Z.: Folk Tales from. Routledge & Kegan Paul Ltd., London (1952)
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Coulter, D., Ehlers, E. (2010). Cellular Automata and Immunity Amplified Stochastic Diffusion Search. In: Bai, Q., Fukuta, N. (eds) Advances in Practical Multi-Agent Systems. Studies in Computational Intelligence, vol 325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16098-1_2
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DOI: https://doi.org/10.1007/978-3-642-16098-1_2
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