Abstract
Population diversity is generally seen as playing a crucial role in the ability of evolutionary computation techniques to discover solutions. In genetic programming, diversity metrics are usually based on structural properties of individual program trees, but are also sometimes based on the spread of fitness values in the population. We explore the use of a further interpretation of diversity, in which differences are measured in terms of the behaviour of programs when executed. Although earlier work has shown that improving behavioural diversity in initial GP populations can have a marked beneficial effect on performance, further analysis reveals that lack of behavioural diversity is a problem throughout whole runs, even when other diversity levels are high. To address this, we enhance phenotypic diversity via modifications to the crossover operator, and show that this can lead to additional performance improvements.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
McPhee, N.F., Hopper, N.J.: Analysis of Genetic Diversity through Program History. In: Banzhaf, W., et al. (eds.) Proc. Genetic and Evolutionary Computation Conf., Florida, USA, pp. 1112–1120 (1999)
Daida, J.M., Ward, D.J., Hilss, A.M., Long, S.L., Hodges, M.R., Kriesel, J.T.: Visualizing the Loss of Diversity in Genetic Programming. In: Proc. IEEE Congress on Evolutionary Computation, Portland, Oregon, USA, pp. 1225–1232 (2004)
Jackson, D.: Phenotypic Diversity in Initial Genetic Programming Populations. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 98–109. Springer, Heidelberg (2010)
Hien, N.T., Hoai, N.X.: A Brief Overview of Population Diversity Measures in Genetic Programming. In: Pham, T.L., et al. (eds.) Proc. 3rd Asian-Pacific Workshop on Genetic Programming, Hanoi, Vietnam, pp. 128–139 (2006)
Burke, E., Gustafson, S., Kendall, G., Krasnogor, N.: Advanced Population Diversity Measures in Genetic Programming. In: Guervos, J.J.M., et al. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 341–350. Springer, Heidelberg (2002)
Burke, E., 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)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
de Jong, E.D., Watson, R.A., Pollack, J.B.: Reducing Bloat and Promoting Diversity using Multi-Objective Methods. In: Spector, L., et al. (eds.) Proc. Genetic Evolutionary Computation Conf., San Francisco, CA, USA, pp. 11–18 (2001)
Wyns, B., de Bruyne, P., Boullart, L.: Characterizing Diversity in Genetic Programming. In: Collet, P., et al. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 250–259. Springer, Heidelberg (2006)
Rosca, J.P.: Genetic Programming Exploratory Power and the Discovery of Functions. In: McDonnell, J.R., et al. (eds.) Proc. 4th Conf., Evolutionary Programming, San Diego, CA, USA, pp. 719–736 (1995)
Rosca, J.P.: Entropy-Driven Adaptive Representation. In: Rosca, J.P. (ed.) Proc. Workshop on Genetic Programming: From Theory to Real-World Applications, Tahoe City, CA, USA, pp. 23–32 (1995)
D’haeseleer, P., Bluming, J.: Effects of Locality in Individual and Population Evolution. In: Kinnear, K.E., et al. (eds.) Advances in Genetic Programming, ch. 8. pp. 177–198. MIT Press, Cambridge (1994)
Ryan, C.: Pygmies and Civil Servants. In: Kinnear, K.E., et al. (eds.) Advances in Genetic Programming, ch.11, pp. 243–263. MIT Press, Cambridge (1994)
Looks, M.: On the Behavioural Diversity of Random Programs. In: Thierens, D., et al. (eds.) Proc. Genetic and Evolutionary Computing Conf. (GECCO 2007), London, England, UK, pp. 1636–1642 (2007)
Beadle, L., Johnson, C.G.: Semantic Analysis of Program Initialisation in Genetic Programming. Genetic Programming and Evolvable Machines 10(3), 307–337 (2009)
Beadle, L., Johnson, C.G.: Semantically Driven Crossover in Genetic Programming. In: Proc. IEEE Congress on Evolutionary Computation (CEC), Hong Kong, pp. 111–116 (2008)
Soule, T.: Code Growth in Genetic Programming. PhD Thesis, University of Idaho (1998)
Langdon, W.B., Soule, T., Poli, R., Foster, J.A.: The Evolution of Size and Shape. In: Spector, L., et al. (eds.) Advances in Genetic Programming, vol. 3, pp. 163–190. MIT Press, Cambridge (1999)
Jackson, D.: Dormant Program Nodes and the Efficiency of Genetic Programming. In: Beyer, H.-G., et al. (eds.) Proc. Genetic and Evolutionary Computing Conf. (GECCO 2005), Washington DC, USA, pp. 1745–1751 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jackson, D. (2010). Promoting Phenotypic Diversity in Genetic Programming. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-15871-1_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15870-4
Online ISBN: 978-3-642-15871-1
eBook Packages: Computer ScienceComputer Science (R0)