Abstract
Population diversity has long been seen as a crucial factor for the efficiency of Evolutionary Algorithms in general, and Genetic Programming (GP) in particular. This paper experimentally investigates the diversity property of a recently proposed crossover, Semantic Similarity based Crossover (SSC). The results show that while SSC helps to improve locality, it leads to the loss of diversity of the population. This could be the reason that sometimes SSC fails in achieving superior performance when compared to standard subtree crossover. Consequently, we introduce an approach to maintain the population diversity by combining SSC with a multi-population approach. The experimental results show that this combination maintains better population diversity, leading to further improvement in GP performance. Further SSC parameters tuning to promote diversity gains even better results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Beadle, L., Johnson, C.: Semantically driven crossover in genetic programming. In: Proceedings of the IEEE World Congress on Computational Intelligence, pp. 111–116. IEEE Press (2008)
Burke, E.K., Gustafson, S., Kendall, G.: Diversity in genetic programming: An analysis of measures and correlation with fitness. IEEE Transactions on Evolutionary Computation 8(1), 47–62 (2004)
Fernandez, F., Tomassini, M., Vanneschi, L.: An empirical study of multipopulation genetic programming. Genetic Programming and Evolvable Machines 4(1), 21–51 (2003)
Gustafson, S., Burke, E.K., Kendall, G.: Sampling of Unique Structures and Behaviours in Genetic Programming. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 279–288. Springer, Heidelberg (2004)
Hoai, N.X., McKay, R.I., Essam, D.: Representation and structural difficulty in genetic programming. IEEE Transaction on Evolutionary Computation 10(2), 157–166 (2006)
Koza, J.: Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, MA (1992)
Langdon, W.B.: Genetic Programming and Data Structures: Genetic Programming + Data Structure = Automatic Programming! Kluwer Academic, Boston (1998)
Looks, M.: On the behavioral diversity of random programs. In: GECCO 2007: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, July 7-11, vol. 2, pp. 1636–1642. ACM Press (2007)
McKay, B.: An investigation of fitness sharing in genetic programming. The Australian Journal of Intelligent Information Processing Systems 7(1/2), 43–51 (2001)
Nguyen, Q.U., Nguyen, X.H., O’Neill, M., McKay, R.I., Galvan-Lopez, E.: Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genetic Programming and Evolvable Machines, 91–119 (2011)
O’Reilly, U.M., Oppacher, F.: Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 397–406. Springer, Heidelberg (1994)
Poli, R., Langdon, W.B.: On the search properties of different crossover operators in genetic programming. In: Genetic Programming: Proceedings of the Third Annual Conference, pp. 293–301. Morgan Kaufmann (1998)
Rosca, J.P.: Entropy-driven adaptive representation. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, July 9, pp. 23–32 (1995)
Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms, 2nd edn. Springer (2006)
Tenese, R.: Parallel genetic algorithms for a hypercube. In: Greenstette, J.J. (ed.) Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pp. 177–183. Lawrence Erlbaum
Tomassini, M., Vanneschi, L., Fernández, F., Galeano, G.: A Study of Diversity in Multipopulation Genetic Programming. In: Liardet, P., Collet, P., Fonlupt, C., Lutton, E., Schoenauer, M. (eds.) EA 2003. LNCS, vol. 2936, pp. 243–255. Springer, Heidelberg (2004)
Nguyen, Q.U., Nguyen, X.H., O’Neill, M.: Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 292–302. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pham, T.A., Nguyen, Q.U., Nguyen, X.H., O’Neill, M. (2013). Examining the Diversity Property of Semantic Similarity Based Crossover. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds) Genetic Programming. EuroGP 2013. Lecture Notes in Computer Science, vol 7831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37207-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-37207-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37206-3
Online ISBN: 978-3-642-37207-0
eBook Packages: Computer ScienceComputer Science (R0)