many Nature Inspired Search techniques there is a comprehensive mathematical framework describing the behaviour of SDS. Analysis of SDS has investigated its global optimality and convergence [Nasuto, 1998], linear time complexity [Nasuto et al., 1999], robustness [Myatt, 2004], and resource allocation [Nasuto, 1999] under a variety of search conditions. References Bishop, J.M. & Torr, P., (1992). The Stochastic Search Network. In R. Linggard, D.J. Myers, C. Nightingale (eds.), Neural Networks for Images, Speech and Natural Language, pp370–387, New York, Chapman & Hall. Beattie, P.D. & Bishop, J.M., (1998). Self-Localisation in the 'Senario' Autonomous Wheelchair. Journal of Intelligent and Robotic Systems 22, pp 255–267, Kluwer Academic Publishers. Grech-Cini, H.J. & McKee, G.T. (1993) Locating the Mouth Region in Images of Human Faces. In P.S.Schenker (Ed.), Proceedings of SPIE – The International Society for Optical Engineering, Sensor Fusion VI 2059, Massachusetts. Myatt, D.R., Bishop J.M. and Nasuto, S.J., (2004). Minimum Stable Convergence Criteria for Stochastic Diffusion Search To be published in Electronics Letters. Nasuto, S.J., (1999). Analysis of Resource Allocation of Stochastic Diffusion Search. PhD Thesis. University of Reading, UK. Nasuto, S.J. & Bishop, J.M., (1999). Convergence Analysis of Stochastic Diffusion Search. Journal of Parallel Algorithms and Applications 14:2, pp 89–107. Nasuto, S.J., Bishop, J.M. & Lauria, L., (1998). Time Complexity of Stochastic Diffusion Search. Neural Computation '98, Vienna, Austria. Whitaker, R.M., Hurley, S., (2002). An agent based approach to site selection for wireless networks. Proc ACM Symposium on Applied Computing (Madrid). 574–577. Jones, D. (2002). Constrained Stochastic Diffusion Search. SCARP 2002, University of Reading, UK. Nature-inspired metaheuristics