Skip to main content
SpringerLink
Log in

Parametric Approximation of Functions Using Genetic Algorithms: An Example with a Logistic Curve

  • Conference paper
Numerical Methods and Applications (NMA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6046))

Included in the following conference series:

Abstract

Whenever we have a set of discrete measures of a phenomenon and try to find an analytic function which models such phenomenon, we are solving a problem about finding some parameters that minimizes a computable error function. In this way, parameter estimation may be studied as an optimization problem, in which the fitness function we are trying to minimize is the error one. This work try to do that using a genetic algorithm to obtain three parameters of a function. Particularly, we use data about one village population over time to see the goodness of our algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  2. Jaffrezic, F., Meza, C., Lavielle, M., Foulley, J.-L.: Genetic analysis of growth curves using the SAEM algorithm. Genet. Sel. Evol. 38(EDP Sciences), 583–600 (2006)

    Google Scholar 

  3. Liu, X.-Y.: An improvement logistic model based on multiple objective genetic algorithm. In: Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, July 12-15, pp. 2292–2295 (2009)

    Google Scholar 

  4. Syswerda, G.: Schedule optimization using genetic algorithms. In: Davis, L. (ed.) Handbook of Genetic Algorithms, pp. 332–349. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  5. Whitley, D.: A genetic algorithm tutorial (2005)

    Google Scholar 

  6. Kuhn, E., Lavielle, M.: Maximum likelihood estimation in nonlinear mixed effects models. Comput. Statist. Data Anal. 49, 1020–1038 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  7. Universidad Nacional de Colombia: Estimación de la Población Futura, http://www.virtual.unal.edu.co/cursos/sedes/manizales/4080004/contenido/Capitulo_4/Pages/caudales_continuacion1.htm

  8. Veres Ferrer, E.: Nuevo procedimiento para el ajuste de la curva logística: aplicación a la población española. Estadística Española 108, 5–17 (1985)

    Google Scholar 

  9. Martínez, E.: Dinámica poblacional (II): la ecuación logística, http://www.uantof.cl/facultades/csbasicas/Matematicas/academicos/emartinez/calculo/poblacion/logistica/logistica.html

  10. Poveda, R.G., Manrique, H.J.: Aplicación de la curva logística a los censos de la ciudad de Medellín. Ecos de Economía (25) (2007)

    Google Scholar 

  11. Vinterbo, S., Ohno-Machado, L.: A Genetic Algorithm to Select Variables in Logistic Regression: Example in the Domain of Myocardial Infarction. In: AMIA, Inc., pp. 984–988 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dep. of Technologies of Computers and Communications, University of Extremadura, Spain

    Fernando Torrecilla-Pinero, Juan A. Gómez-Pulido, Miguel A. Vega-Rodríguez & Juan M. Sánchez-Pérez

  2. Dep. of Building, University of Extremadura, Spain

    Jesús A. Torrecilla-Pinero

Authors
  1. Fernando Torrecilla-Pinero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jesús A. Torrecilla-Pinero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan A. Gómez-Pulido
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miguel A. Vega-Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juan M. Sánchez-Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev 25 A, 1113, Sofia, Bulgaria

    Ivan Dimov

  2. Faculty of Mathematics and Informatics, Department Numerical Methods and Algorithms, University of Sofia "St. Kliment Ohridski",, blvd. James Bourchier 5, 1164, Sofia, Bulgaria

    Stefka Dimova

  3. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. Bonchev St.,Bl.8, 1113, Sofia, Bulgaria

    Natalia Kolkovska

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Torrecilla-Pinero, F., Torrecilla-Pinero, J.A., Gómez-Pulido, J.A., Vega-Rodríguez, M.A., Sánchez-Pérez, J.M. (2011). Parametric Approximation of Functions Using Genetic Algorithms: An Example with a Logistic Curve. In: Dimov, I., Dimova, S., Kolkovska, N. (eds) Numerical Methods and Applications. NMA 2010. Lecture Notes in Computer Science, vol 6046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18466-6_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-18466-6_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18465-9

  • Online ISBN: 978-3-642-18466-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation