Abstract
In recent years several strategies for inferring gene regulatory networks from observed time series data of gene expression have been suggested based on Evolutionary Algorithms. But often only few problem instances are investigated and the proposed strategies are rarely compared to alternative strategies. In this paper we compare Evolution Strategies and Genetic Programming with respect to their performance on multiple problem instances with varying parameters. We show that single problem instances are not sufficient to prove the effectiveness of a given strategy and that the Genetic Programming approach is less prone to varying instances than the Evolution Strategy.
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.
Similar content being viewed by others
References
Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the boolean network model. Pacific Symposium on Biocomputing 4, 17–28 (1999)
Akutsu, T., Miyano, S., Kuhara, S.: Algorithms for identifying boolean networks and related biological networks based on matrix multiplication and fingerprint function. Journal of Computational Biology 7(3), 331–343 (2000)
Chen, T., He, H.L., Church, G.M.: Modeling gene expression with differential equations. Pacific Symposium on Biocomputing 4, 29–40 (1999)
Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaption. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pp. 312– 317 (1996)
Koza, J.R., Andre, D., Bennett III, F.H., Keane, M.: Genetic Programming 3: Darwinian Invention and Problem Solving. Morgan Kaufman, San Francisco ( April 1999)
Mendes, P., Sha, W., Ye, K.: Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics 19(2), 122–129 (2003)
Morishita, R., Imade, H., Ono, I., Ono, N., Okamoto, M.: Finding multiple solutions based on an evolutionary algorithm for inference of genetic networks by s-system. In: Congress on Evolutionary Computation, pp. 615–622 (2003)
Sakamoto, E., Iba, H.: Inferring a system of differential equations for a gene regulatory network by using genetic programming. In: Proceedings of Congress on Evolutionary Computation, pp. 720–726. IEEE Press, Los Alamitos (27-30, 2001)
Savageau, M.: 20 years of S-systems. In: Voit, E. (ed.) Canonical Nonlinear Modeling. S-systems Approach to Understand Complexity, New York, pp. 1–44. Van Nostrand Reinhold, New York (1991)
Schwefel, H.-P.: Evolution and Optimum Seeking. John Wiley & Sons, New York (1995)
Spieth, C., Streichert, F., Speer, N., Zell, A.: Iteratively inferring gene regulatory networks with virtual knockout experiments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 102–111. Springer, Heidelberg (2004)
Thieffry, D., Thomas, R.: Qualitative analysis of gene networks. Pacific Symposium on Biocomputing 3, 77–88 (1998)
Tominaga, D., Kog, N., Okamoto, M.: Efficient numerical optimization technique based on genetic algorithm for inverse problem. In: Proceedings of German Conference on Bioinformatics, pp. 127–140 (1999)
Tominaga, D., Okamoto, M., Maki, Y., Watanabe, S., Eguchi, Y.: Nonlinear numerical optimzation technique based on genetic algorithm for inverse problem: Towards the inference of genetic networks. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 251–258. Morgan Kaufmann, San Francisco (2000)
Wahde, M., Hertz, J.: Coarse-grained reverse engineering of genetic regulatory networks. Biosystems 55, 129–136 (2000)
Weaver, D.C., Workman, C.T., Stormo, G.D.: Modeling regulatory networks with weight matrices. In: Pacific Symposium on Biocomputing, Singapore, vol. 4, pp. 112–123. World Scientific Press, Singapore (1999)
Wuensche, A.: Genomic regulation modeled as a network with basins of attraction. In: Altman, R., Dunker, A., Hunter, L., Klein, T. (eds.) Pacific Symposium on Biocomputing, Singapore, vol. 3, pp. 89–102. World Scientific Press, Singapore (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Streichert, F., Planatscher, H., Spieth, C., Ulmer, H., Zell, A. (2004). Comparing Genetic Programming and Evolution Strategies on Inferring Gene Regulatory Networks. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24854-5_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-24854-5_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22344-3
Online ISBN: 978-3-540-24854-5
eBook Packages: Springer Book Archive