In this work we investigated some new aspects of a recently introduced hybrid method which was a combination of Genetic algorithm, Monte Carlo integration schema and variational method. We also added some new features to the method in order to reduce the computational costs. Now we have introduced the biased Genetic Monte Carlo Variational (BGMV). With the help of different components of the method like initial physical and computational parameters we have tried to find a more trustworthy method for nanostructure investigations. It is shown that criterions like saturation of a quantity with respect to different parameters of the Genetic Algorithm like number of Genetic iterations may not lead to accurate results. CPU time of the program as a function of the number of genetic iterations for different elitist percent is depicted. Exciton binding energy of GaAs0.7Sb0.3/GaAs is obtained.
Key words: Genetic algorithm, variational method, Monte Carlo integration scheme, quantum well, exciton binding energy.
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