Abstract
In tree-based genetic programming (GP) performance optimization, the primary optimization target is the process of fitness evaluation. This is because fitness evaluation takes most of execution time in GP. Standard fitness evaluation uses the top-down tree evaluation algorithm. Top-down tree evaluation evaluates program tree from the root to the leaf of the tree. The algorithm reflects the nature of computer program execution and hence it is the most widely used tree evaluation algorithm. In this paper, we identify a scenario in tree evaluation where top-down evaluation is costly and less effective. We then propose a new tree evaluation algorithm called bottom-up tree evaluation explicitly addressing the problem identified. Both theoretical analysis and practical experiments are performed to compare the performance of bottom-up tree evaluation and top-down tree evaluation. It is found that bottom-up tree evaluation algorithm outperforms standard top-down tree evaluation when the program tree depth is small.
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
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)
Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming (2008)
Keijzer, M.: Alternatives in subtree caching for genetic programming. In: EuroGP, pp. 328–337 (2004)
Tackett, W.A.: Recombination, selection, and the genetic construction of computer programs. PhD thesis, Los Angeles, CA, USA (1994)
Jackson, D.: Fitness evaluation avoidance in boolean GP problems. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Edinburgh, UK, vol. 3, pp. 2530–2536. IEEE Press, Los Alamitos (2005)
Giacobini, M., Tomassini, M., Vanneschi, L.: Limiting the number of fitness cases in genetic programming using statistics. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 371–380. Springer, Heidelberg (2002)
Xie, H., Zhang, M., Andreae, P.: Population clustering in genetic programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 190–201. Springer, Heidelberg (2006)
Li, G.: A novel approach to model genetic programming behaviors based on activation rate. Research Report, School of Computer Science, University of Manchester (2009)
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
Li, G., Zeng, Xj. (2010). Bottom-Up Tree Evaluation in Tree-Based Genetic Programming. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_63
Download citation
DOI: https://doi.org/10.1007/978-3-642-13495-1_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13494-4
Online ISBN: 978-3-642-13495-1
eBook Packages: Computer ScienceComputer Science (R0)