Advertisement

A Genetic Algorithm Applied to a Main Sequence Stellar Model

  • Gabriela de Oliveira Penna Tavares
  • Marco Aurelio Cavalcanti Pacheco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6678)

Abstract

The purpose of this work is to determine some structural properties of main sequence stars through the utilization of a genetic algorithm (GA) and observational data. Genetic algorithms (GAs) are a Computational Intelligence technique inspired by Charles Darwin’s theory of evolution, used to optimize the solution of problems for which there are many variables, complex mathematical modeling or a large search space. Here, we apply this technique to approximate certain aspects of stellar structure that cannot be determined through observation: the mass, radius, core density, core temperature and core pressure of a main sequence star. In order to achieve this, we use an observational value for the star’s luminosity (energy flux released on the surface). Alternatively, an observational value for the star’s surface temperature can be used. A mathematical model for the star’s internal structure is needed to evaluate the adequacy of each solution proposed by the algorithm.

Keywords

Computational Intelligence genetic algorithm main sequence star structure stellar model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Prialnik, D.: An Introduction to the Theory of Stellar Structure and Evolution. Cambridge University Press, Cambridge (2010)Google Scholar
  2. 2.
    Phillips, A.C.: The Physics of Stars. Wiley, Chichester (2010)Google Scholar
  3. 3.
    Tayler, R.J.: Stars: their Structure and Evolution. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  4. 4.
    Freeman, R.A., Kauffman, W.J.: Universe. W.H. Freeman, New York (2008)Google Scholar
  5. 5.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)CrossRefzbMATHGoogle Scholar
  6. 6.
    Corchado, E., Abraham, A., de Carvalho, A.C.: Hybrid Intelligent Algorithms and Applications. Information Science (2010)Google Scholar
  7. 7.
    Charbonneau, P.: Genetic Algorithms in Astronomy and Astrophysics. ApJS 101, 309 (1995)CrossRefGoogle Scholar
  8. 8.
    Mokiem, M.R., De Koter, A., Puls, J., Herrero, A., Najarro, F., Villamariz, M.R.: Spectral analysis of early-type stars using a genetic algorithm based fitting method. A&A 441(2), 711–733 (2005)CrossRefGoogle Scholar
  9. 9.
    Metcalfe, T.S., Charbonneau, P.: Stellar Structure Modeling using a Parallel Genetic Algorithm for Objective Global Optimization. JCP 185(1), 176–193 (2003)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gabriela de Oliveira Penna Tavares
    • 1
  • Marco Aurelio Cavalcanti Pacheco
    • 1
  1. 1.ICA – Pontifical Catholic University of Rio de JaneiroBrazil

Personalised recommendations