An empirical investigation into correlation functions in a spatially-dispersed evolutionary algorithm

Spatially-structured populations play an important role in controlling selection pressure in evolutionary algorithms. The imposing space on a evolving population has traditionally been biased toward the underlying architecture that the population is executed on. The spatially dispersed evolutionary algorithm (sdEA) is an attempt to model population structures incorporating more probabilistic measures into the construction of demes. One important component of the sdEA in determining demes is the correlation function. This paper introduces three new correlation functions into the realm of the sdEA and compares their resultant behaviours on four differing test cases. Initial results indicate that the design of a correlation function should bias deme construction to small areas in the population surface.PublishedNon Peer ReviewedAshlock, D. & Lathrop, J. I. (1998). “A Fully Characterized Test Suite for Genetic Programming” In V. W. Porto, N. Saravanan, D. Waagen & A. E. Eiben (eds), Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming. Vol. 1447 of LNCS Springer-Verlag Mission Valley Marriott, San Diego, California, USA pp. 537–546. Bryden, K. M., Ashlock, D. A. & Corns, S. M. (2003). “Graph Based Evolutionary Algorithms” Technical report. Department of Mechanical Engineering, Iowa State University. In revision for submission to IEEE Transactions on Evolutionary Computation. Dick, G. (2003a). “The Spatially-Dispersed Genetic Algorithm” In E. Cantú-Paz (ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2003. Springer Verlag. Dick, G. (2003b). “The Spatially Dispersed Genetic Algorithm: An Explicit Spatial Model for GAs” In R. Sarker, R. Reynolds, H. Abbass, K. C. Tan, B. McKay, D. Essam & T. Gedeon (eds), Proceedings of the 2003 Congress on Evolutionary Computation CEC2003. IEEE Press Canberra pp. 2445–2461. Dick, G. & Whigham, P. (2002). “Spatially constrained selection in evolutionary computation” Australia-Japan Joint Workshop on Intelligent and Evolving Systems.. Folino, G., Pizzuti, C., Spezzano, G., Vanneschi, L. & Tomassini, M. (2003). “Diversity analysis in cellular and multipopulation genetic programming” In R. Sarker, R. Reynolds, H. Abbass, K. C. Tan, B. McKay, D. Essam & T. Gedeon (eds), Proceedings of the 2003 Congress on Evolutionary Computation CEC2003. IEEE Press Canberra pp. 305–311. Mühlenbein, H. (1991). “Evolution in Time and Space – The Parallel Genetic Algorithm” In G. J. Rawlins (ed.), Foundations of Genetic Algorithms. Morgan Kaufmann Publishers. pp. 316–337. Whitley, D., Rana, S., Dzubera, J. & Mathias, K. E. (1996). “Evaluating evolutionary algorithms” Artif. Intell.. 85(1-2): 245–276