| Abstract: |
| This paper introduces a machine learning framework using differential evolution algorithm to learn the perturbation parameter ($\epsilon^{\alpha}$) involved in the singularly perturbed problems. Perturbation parameter plays a vital role in the field of the perturbation theory and is responsible for generating boundary/interior layers. Initially, random data is being used as an population for the estimation of the parameter ($\alpha$). Two types of problems: convection diffusion and reaction diffusion, have been tested for the estimation of the parameter. For comparison, the same test problems have also been solved using the particle swarm optimization method. For the values of the various parameters, results have been shown in the tables along with best cost figures. |
|