Efficiency of Genetic Algorithm and Determination of Ground State Energy of Impurity in a Spherical Quantum Dot

Abstract

In the present work, genetic algorithm method (GA) is applied to the problem of impurity at the center of a spherical quantum dot for infinite confining potential case. For this purpose, any trial variational wave function is considered for the ground state and energy values are calculated. In applying the GA to the problem under investigation, two different approaches were followed. Furthermore, a standard variational procedure is also performed to determine the energy eigenvalues. The results obtained by all methods are found in satisfactory agreement with each other and also with the exact values in literature. But, it is found that the values obtained by genetic algorithm based upon wavefunction optimization are closer to the exact values than standard variational and also than genetic algorithm based on parameter optimization methods.

Computation of the oscillator strength and absorption coefficients for the intersubband transitions of the spherical quantum dot

“…Therefore, many authors have studied to the electronic structure, the ground and excited energy states, the binding energy, the relativistic effects etc. of the spherical QDs by using various methods such as perturbation [1], variational [2][3][4][5], exact solution [6,7], QGA [8][9][10] and a combination of QGA and HFR method [11,12].…”

Application of genetic algorithm in extracting cell dielectric characteristics with electrorotation

“…In addition, some traits are changed during a rarer process called mutation. All the parameters can be altered simultaneously in the GA method; therefore, it can obtain a faster convergence [20].…”

Self-Consistent Calculation of Semiconductor Heterojunctions Using Quantum Genetic Algorithm

“…Two new functions are produced by using these individuals as where cr(z) is a smooth step function 27,34 or its value can be randomly selected from a uniform distribution between (0, 1). At each step of the GA iteration, the kind of crossover (namely the selection of the smooth step function or a random real number) is randomly determined.…”