Continuous optimization using elite genetic algorithms with adaptive mutations

Abstract

The elite genetic algorithm with adaptive mutations is applied to two different continuous optimization problems: determination of model parameters of optical constants of aluminum and thin film optical filter design. The concept of adaptive mutations makes the employed algorithm a versatile tool for solving continuous optimization problems. The algorithm has been successful in solving both investigated problems. In determination of optical constants of aluminum, excellent agreement between calculated and experimental data is obtained. In application to thin film optical filter design, low-pass filters designed using this algorithm are clearly superior to filters designed using the traditional approach.