GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE

In this current technological era, engineering design process encounters with multiple complexity as it depends on computer simulation to ensure accurate model before the model is actually constructed. This leads to the problems where the engineers have to feed their simulation with new inputs and parameters to get the best solution. Specifically, in electromagnetic field, bidirectional scattering distribution function of a diffraction grating is computed using MEEP simulation and requires numerous numbers of parameters. This paper will report on an initial study of the usage of Genetic Algorithm (GA) merged with Deep Neural Network based surrogate model to optimize simulation for electromagnetic structure. The behavior of Genetic Algorithm (GA) where it generates and evolves the parameters towards a high-quality solution gives an advantage in obtaining ideal combination of parameters to fit in with the simulation. The GA will be assisted with the convergence of deep neural network to increase the performance by reducing the computational time