A Genetic Algorithm Applied to a Main Sequence Stellar Model
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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.
KeywordsComputational Intelligence genetic algorithm main sequence star structure stellar model
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