A genetic algorithm applied to a main sequence stellar model

Authors:
Gabriela de Oliveira Penna Tavares;Marco Aurelio Cavalcanti Pacheco
Affiliations:
ICA, Pontifical Catholic University of Rio de Janeiro, Brazil;ICA, Pontifical Catholic University of Rio de Janeiro, Brazil
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Year:
2011

Citing 3
Cited 0

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Stellar structure modeling using a parallel genetic algorithm for objective global optimization

Journal of Computational Physics
Editorial: Hybrid intelligent algorithms and applications

Information Sciences: an International Journal

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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.