Abstract
Genetic Programming (GP) has been shown to be a good method of predicting functions that solve inverse problems. In this context, a solution given by GP generally consists of a sole predictor. In contrast, Stack-based GP systems manipulate structures containing several predictors, which can be considered as teams of predictors. Work in Machine Learning reports that combining predictors gives good results in terms of both quality and robustness. In this paper, we use Stack-based GP to study different cooperations between predictors. First, preliminary tests and parameter tuning are performed on two GP benchmarks. Then, the system is applied to a real-world inverse problem. A comparative study with standard methods has shown limits and advantages of teams prediction, leading to encourage the use of combinations taking into account the response quality of each team member.
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References
Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Transactions on Evolutionary Computation 5(1), 17–26 (2001)
Brameier, M., Banzhaf, W.: Evolving teams of predictors with linear genetic programming. Genetic Programming and Evolvable Machines 2(4), 381–407 (2001)
Bruce, W.S.: The lawnmower problem revisited: Stack-based genetic programming and automatically defined functions. In: Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, 13-16 1997, pp. 52–57. Morgan Kaufmann, San Francisco (1997)
Chami, M., Robilliard, D.: Inversion of oceanic constituents in case i and case ii waters with genetic programming algorithms. Applied Optics 40(30), 6260–6275 (2002)
Chami, M., Santer, R., Dilligeard, E.: Radiative transfer model for the computation of radiance and polarization in an ocean-atmosphere system: polarization properties of suspended matter for remote sensing. Applied Optics 40(15), 2398–2416 (2001)
Collet, P., Lutton, E., Raynal, F., Schoenauer, M.: Polar IFS+parisian genetic programming=efficient IFS inverse problem solving. Genetic Programming and Evolvable Machines 1(4), 339–361 (2000)
Gross, L., Thiria, S., Frouin, R., Mitchell, B.G.: Artificial neural networks for modeling the transfer function between marine reflectances and phytoplankton pigment concentration. J. Geophys. Res. C2(105), 3483–3495 (2000)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. NIPS 7, 231–238 (1995)
Langdon, W.B., Banzhaf, W.: Repeated sequences in linear gp genomes. In: Late breaking paper at GECCO 2004, Seattle, USA (June 2004)
Paris, G., Robilliard, D., Fonlupt, C.: Applying boosting techniques to genetic programming. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 267–278. Springer, Heidelberg (2002)
Perkis, T.: Stack-based genetic programming. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, Orlando, Florida, USA, 27-29 1994, vol. 1, pp. 148–153. IEEE Press, Los Alamitos (1994)
Poli, R.: A simple but theoretically-motivated method to control bloat in genetic programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 200–210. Springer, Heidelberg (2003)
Sarle, W.: Stopped training and other remedies for overfitting. In: Proceedings of the 27th Symposium on Interface (1995)
Stoffel, K., Spector, L.: High-performance, parallel, stack-based genetic programming. In: Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, 28–31 1996, pp. 224–229. MIT Press, Cambridge (1996)
Yao, X.: Universal approximation by genetic programming. In: Foundations of Genetic Programming, Orlando, Florida, USA, 13 (1999)
Zhang, B.-T., Joung, J.-G.: Time series prediction using committee machines of evolutionary neural trees. In: Proceedings of the Congress of Evolutionary Computation, vol. 1, pp. 281–286. IEEE Press, Los Alamitos (1999)
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Platel, M.D., Chami, M., Clergue, M., Collard, P. (2005). Teams of Genetic Predictors for Inverse Problem Solving. In: Keijzer, M., Tettamanzi, A., Collet, P., van Hemert, J., Tomassini, M. (eds) Genetic Programming. EuroGP 2005. Lecture Notes in Computer Science, vol 3447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31989-4_31
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DOI: https://doi.org/10.1007/978-3-540-31989-4_31
Publisher Name: Springer, Berlin, Heidelberg
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