Abstract
When researchers make alterations to the genetic programming algorithm they almost invariably wish to measure the change in performance of the evolutionary system. No one specific measure is standard, but Koza’s computational effort statistic is frequently used [8]. In this paper the use of Koza’s statistic is discussed and a study is made of three methods that produce confidence intervals for the statistic. It is found that an approximate 95% confidence interval can be easily produced.
This is a preview of subscription content, log in via an institution to check access.
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
Andre, D., Koza, J.R.: Parallel genetic programming on a network of transputers. In: Rosca, J.P. (ed.) Proceedings of the Workshop on Genetic Programming, pp. 111-120 (1995)
Angeline, P.J.: An investigation into the sensitivity of genetic programming to the frequency of leaf selection during subtree crossover. In: Proceedings of the First Annual Conference on Genetic Programming (1996)
Christensen, S., Oppacher, F.: An analysis of Koza’s computational effort statistic for genetic programming. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, Springer, Heidelberg (2002)
Clarke, G., Cooke, D.: A Basic Course in Statistics, 4th edn. Arnold (1998)
Keijzer, M., Babovic, V., Ryan, C., O’Neill, M., Cattolico, M.: Adaptive logic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 42–49. Morgan Kaufmann, Seattle (2001)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)
Luke, S., Panait, L.: Is the perfect the enemy of the good? In: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Seattle (2002)
Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.): EuroGP 2000. LNCS, vol. 1802. Springer, Heidelberg (2000)
Newcombe, R.G.: Two-sided confidence intervals for the single proportion: comparison of seven methods. Statistics in Medicine 17, 857–872 (1998)
Niehaus, J., Banzhaf, W.: More on computational effort statistics for genetic programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Walker, M., Edwards, H., Messom, C. (2007). Confidence Intervals for Computational Effort Comparisons. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-71605-1_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71602-0
Online ISBN: 978-3-540-71605-1
eBook Packages: Computer ScienceComputer Science (R0)