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Modeling Pheromone Dispensers Using Genetic Programming

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Applications of Evolutionary Computing (EvoWorkshops 2009)

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Abstract

Mating disruption is an agricultural technique that intends to substitute the use of insecticides for pest control. This technique consists of the diffusion of large amounts of sexual pheromone, so that the males are confused and mating is disrupted. Pheromones are released using devices called dispensers. The speed of release is, generally, a function of time and atmospheric conditions such as temperature and humidity. One of the objectives in the design of the dispensers is to minimise the effect of atmospheric conditions in the performance of the dispenser. With this objective, the Centro de Ecología Química Agrícola (CEQA) has designed an experimental dispenser that aims to compete with the dispensers already in the market. The hypothesis we want to validate (and which is based on experimental results) is that the performance of the CEQA dispenser is independent of the atmospheric conditions, as opposed to the most widely used commercial dispenser, Isomate CPlus. This was done using a genetic programming (GP) algorithm. GP evolved functions able to describe the performance of both dispensers and that support the initial hypothesis.

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References

  1. Witzgall, P., Bengtsson, M., Rauscher, S., Liblikas, I., Bäckman, A.C., Coracini, M., Anderson, P., Löfqvist, J.: Indentification of further sex pheromones in the codling moth, cydia pomonella. Entomologia Experimentalis et Applicata 101, 131–141 (2001)

    Article  Google Scholar 

  2. Barnes, M., Millar, J., Kirsch, P., Hawks, D.: Codling moth (lepidoptera: Tortricidae) control by dissemination of synthetic female sex pheromone. Journal of Economic Entomology 85, 1274–1277 (1992)

    Article  Google Scholar 

  3. Cardé, R., Minks, A.: Control of moth pest by mating disruption: successes and constraints. Annual Review of Entomology 40, 559–585 (1995)

    Article  Google Scholar 

  4. Witzgall, P., Stelinski, L., Gut, L., Thomson, D.: Codling moth management and chemical ecology. Annual Review of Entomology 53, 503–522 (2008)

    Article  Google Scholar 

  5. Welter, S., Pickel, C., Millar, J., Cave, F., Van Steenwyk, R., Dunley, J.: Pheromone mating disruption offer selective management options for key pest. California Agriculture 59, 16–22 (2005)

    Article  Google Scholar 

  6. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  7. Keijzer, M.: Scaled symbolic regression. Genetic Programming and Evolvable Machines 5, 259–269 (2004)

    Article  Google Scholar 

  8. Gustafson, S., Burke, E., Krasnogor, N.: On improving genetic programming for symbolic regression. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 1, pp. 912–919. IEEE Press, Los Alamitos (2005)

    Chapter  Google Scholar 

  9. Gutiérrez, P., López-Granados, F., Peña Barragán, J.M., Jurado-Expósito, M., Hervás-Martínez, C.: Logistic regression product-unit neural networks for mapping ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Computers and Electronics in Agriculture 64, 293–306 (2008)

    Article  Google Scholar 

  10. Kotanchek, M., Smits, G., Kordon, A.: Industrial strength genetic programming. In: Genetic Programming Theory and Practice, pp. 239–255. Kluwer Academics, Dordrecht (2003)

    Chapter  Google Scholar 

  11. EClab - George Mason University (ECJ), http://cs.gmu.edu/~eclab/projects/ecj

  12. Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation 3, 199–230 (1995)

    Article  Google Scholar 

  13. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Alfaro-Cid, E., Esparcia-Alcázar, A.I., Moya, P., Femenia-Ferrer, B., Sharman, K., Merelo, J.J. (2009). Modeling Pheromone Dispensers Using Genetic Programming. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2009. Lecture Notes in Computer Science, vol 5484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01129-0_73

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  • DOI: https://doi.org/10.1007/978-3-642-01129-0_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01128-3

  • Online ISBN: 978-3-642-01129-0

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