GENETIC ALGORITHM BASED ON DIFFERENTIAL EVOLUTION WITH VARIABLE LENGTH - Runoff Prediction on an Artificial Basin

Ana Freire, Vanessa Aguiar-Pulido, Juan R. Rabuñal, Marta Garrido

2010

Abstract

Differential evolution is a successful approach to solve optimization problems. The way it performs the creation of the individual allows a spontaneous self-adaptability to the function. In this paper, a new method based on the differential evolution paradigm has been developed. An innovative feature has been added: the variable length of the genotype, so this approach can be applied to predict special time series. This approach has been tested over rainfall data for real-time prediction of changing water levels on an artificial basin. This way, a flood prediction system can be obtained.

References

  1. Bownlee, J., 2007. Clonal Selection Algorithms, Complex Intelligent Systems Laboratory (CIS), Centre for Information Technology Research (CITR), Faculty of Information and Communication Technologies (ICT), Swinburne University of Technology, Melbourne, Australia, pp. 13.
  2. Burnet, F. M., 1959. The clonal selection theory of acquired immunity. Vanderbilt University Press, Nashville, Tenessee.
  3. Burnet, F. M., 1976. A modification of Jerne's theory of antibody production using the concept of clonal selection. CA Cancer J Clin 26, 119-21.
  4. Burnet, F. M., Clonal selection and after, in: Bell, G. I., et al., Eds.), Theoretical Immunology, Marcel Dekker Inc. 1978, pp. 63-85.
  5. Cea, L., Garrido, M., Puertas, J., 2099. Urban flood computations from direct precipitation data using a two-dimensional shallow water model. The 8th International Conference on Urban Drainage Modelling. Japón.
  6. Cutello, V., Narzisi, G., Nicosia, G., and Pavone ,M., 2005. Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials, in: Jacob, C. and al., e., Eds.), 4th International Conference on Artificial Immune Systems (ICARIS), Vol. LNCS 3627. Springer-Verlag, Banff, Alberta, Canada, pp. 13-28.
  7. Cutello, V., Narzisi, G., Nicosia, G., Pavone, M., and Sorace, G., 2004. How to Escape Traps using Clonal Selection Algorithms, 1st International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vol. 1. INSTICC Press, Setubal, Portugal, pp. 322-326.
  8. Dorado, J., Rabuñal, J. R., Pazos, A., Rivero, D. and Santos, A., 2003. Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ANN and GP. Applied Artificial Intelligence, vol. 17, pp.329-343.
  9. Drecourt, J. P., 1999. Application of Neural Networks and Genetic Programming to Rainfall-Runoff Modelling. D2K Technical Report, vol. 0699-1-1, Danish Hydraulic Institute, Denmark.
  10. Feoktistov, V., 2006. Differential Evolution. In search of solutions. Springer.
  11. Hydroworks, 1995. User Manual. Hydraulic Research Ltd: Wallingford.
  12. HEC-HMS, http://www.hec.usace.army.mil/software/hechms/, Accedida por última vez: Abril 2010.
  13. Koza, J. R., 1992. Genetic Programming. On the Programming of Computers by means of Natural Selection. Cambrige, MA: The MIT Press.
  14. Mayer, D. G., Kinghorn, B. P. and Archer, A. A., Differential evolution - an easy and efficient evolutionary algorithm for model optimization, Agricultural Systems, vol. 83, pp. 315-328, 2004.
  15. Qin, A. K., Huang, V. L. and Suganthan, P. N., 2009. Differential Evolution Algorithm with strategy adaptation for global numerical optimization, IEEE Trans. On Evolutionary Computation, vol. 13, no. 2, pp. 398-417.
  16. Rabuñal, J. R., Puertas, J., Suárez, J., Rivero, D., 2007. Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrological Processes, vol. 21, pp. 476-485.
  17. Storn, R. and Price, K. V., 1997. Differential evolution-A simple and efficient heuristic for global optimization over continuous Spaces, J. Global Optim., vol. 11, pp. 341-359.
  18. Viessmann, W., Lewis, G. L. and Knapp, J. W., 1989. Introduction to Hydrology. New York: Harper Collins.
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Paper Citation


in Harvard Style

Freire A., Aguiar-Pulido V., Rabuñal J. and Garrido M. (2010). GENETIC ALGORITHM BASED ON DIFFERENTIAL EVOLUTION WITH VARIABLE LENGTH - Runoff Prediction on an Artificial Basin . In Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010) ISBN 978-989-8425-31-7, pages 207-212. DOI: 10.5220/0003081402070212


in Bibtex Style

@conference{icec10,
author={Ana Freire and Vanessa Aguiar-Pulido and Juan R. Rabuñal and Marta Garrido},
title={GENETIC ALGORITHM BASED ON DIFFERENTIAL EVOLUTION WITH VARIABLE LENGTH - Runoff Prediction on an Artificial Basin},
booktitle={Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)},
year={2010},
pages={207-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003081402070212},
isbn={978-989-8425-31-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)
TI - GENETIC ALGORITHM BASED ON DIFFERENTIAL EVOLUTION WITH VARIABLE LENGTH - Runoff Prediction on an Artificial Basin
SN - 978-989-8425-31-7
AU - Freire A.
AU - Aguiar-Pulido V.
AU - Rabuñal J.
AU - Garrido M.
PY - 2010
SP - 207
EP - 212
DO - 10.5220/0003081402070212