Advertisement

Genetic Algorithms for Estimating Longest Path from Inherently Fuzzy Data Acquired with GPS

  • José Villar
  • Adolfo Otero
  • José Otero
  • Luciano Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

Measuring the length of a path that a taxi must fare is an obvious task: when driving lower than certain speed threshold the fare is time dependent, but at higher speeds the length of the path is measured, and the fare depends on such measure. When passing an indoor MOT test, the taximeter is calibrated simulating a cab run, while the taxi is placed on a device equipped with four rotating steel cylinders in touch with the drive wheels. This indoor measure might be inaccurate, as the information given by the cylinders is affected by tires inflating pressure, and only straight trajectories are tested. Moreover, modern vehicles with driving aids such as ABS, ESP or TCS might have their electronics damaged in the test, since two wheels are spinning while the others are not. To surpass these problems, we have designed a small, portable GPS sensor that periodically logs the coordinates of the vehicle and computes the length of a discretionary circuit. We will show that all the legal issues with the tolerance of such a procedure (GPS data are inherently imprecise) can be overcome if genetic and fuzzy techniques are used to process and analyze the raw data.

Keywords

Global Position System Fuzzy Number Longe Path Global Position System Data Fuzzy Random Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anile, A.M., Falcidieno, B., Gallo, G., Spagnuolo, M., Spinello, S.: Modeling uncertain data with fuzzy B-splines. Fuzzy Sets and Systems 113, 397–410 (2000)CrossRefMathSciNetMATHGoogle Scholar
  2. Berman, P., Schnitger, G.: On the complexity of approximating the independent set problem. Inform. and Comput. 96, 77–94 (1992)CrossRefMathSciNetMATHGoogle Scholar
  3. Coello, C.A.: An Updated Survey of Evolutionary Multiobjective Optimization Techniques: State of the Art and Future Trends. Congress on Evolutionary Computation. IEEE Service Center, Los Alamitos (1999)Google Scholar
  4. Couso, I., Montes, S., Gil, P.: The necessity of the strong alpha-cuts of a fuzzy set International Journal of Uncertainty. Fuzziness and Knowledge-Based Systems 9(2), 249–262 (2001)MathSciNetMATHGoogle Scholar
  5. Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) Proceedings of the Parallel Problem Solving from Nature VI Conference. LNCS, pp. 849–858. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. Deb, K., Goel, T.: Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence. In: Zitzler, E., Deb, K., Thiele, L., Coello, C.A.C., Corne, D. (eds.) First International Conference on Evolutionary Multi-Criterion Optimization. LNCS, vol. 1993, pp. 67–81. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. Estkowski, R., Mitchell, J.S.B.: Simplifying a polygonal subdivision while keeping it simple. In: SCG 2001: Proceedings of the seventeenth annual symposium on Computational geometry, pp. 40–49. ACM Press, New York (2002) ISBN 1-58113-357-XGoogle Scholar
  8. Farina, M., Amato, P.: Fuzzy Optimality and Evolutionary Multiobjective Optimization. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 58–72. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. Goodman, N.: Uncertainty Models for Knowledge-based Systems. North- Holland, Amsterdam (1985)MATHGoogle Scholar
  10. Hapke, M., Jaszkiewicz, A., Slowinski, R.: Pareto Simulated Annealing for Fuzzy Multi-Objective Combinatorial Optimization. Journal of Heuristics 6(3), 329–345 (2000)CrossRefMATHGoogle Scholar
  11. Köppen, M., Franke, K., Nickolay, B.: Fuzzy-Pareto Dominance Driven Multiobjective Genetic Algorithm. In: Proceedings of the 10th IFSAWorld Congress (IFSA 2003), Istanbul, Turkey, pp. 450–453 (2003)Google Scholar
  12. Meratnia, N., de By, R.A.: Trajectory representation in location-based services: problems and solutions. In: Proceedings of the 3rd IEEE Workshop on Web and Wireless Geographical Systems (W2GIS 2003) in conjunction with the Fourth International Conference on Web Information Systems Engineering (WISE), Rome, Italy (2003)Google Scholar
  13. Otero, A., Otero, J., Sánchez, L., Villar, J.R.: Longest path estimation from inherently fuzzy data acquired with GPS using genetic algorithms. In: 2nd International Symposium on Evolving Fuzzy Systems, University of Lancaster, UK (2006)Google Scholar
  14. Sánchez, L., Otero, J., Villar, J.R.: Boosting of fuzzy models for high-dimensional imprecise datasets. In: Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU06, Paris (2006)Google Scholar
  15. Snyder, J.P.: Map Projections Used by the U. S. Geological Survey, 2nd edn., Geol. Survey Bulletin 1532, p. 313, U. S. Government Printing Office, Washington, D. C (1982)Google Scholar
  16. Wilson, D.: David L. Wilson’s GPS Accuracy Web Page, http://users.erols.com/dlwilson/gps.html
  17. Zhang, J., Pham, B., Chen, P.: Fuzzy Genetic Algorithms Based on Level Interval Algorithm. In: Kazmierczak, E (ed.) Proceedings The 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia, pp. 1424–1427 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • José Villar
    • 1
  • Adolfo Otero
    • 1
  • José Otero
    • 1
  • Luciano Sánchez
    • 1
  1. 1.Computer Science DepartmentUniversidad de OviedoGijonSpain

Personalised recommendations