Electromagnetic optimization using a mixed-parameter self-adaptive evolutionary algorithm

An evolutionary programming algorithm with a mixed continuous-discrete parameter representation for application in electromagnetic optimization problems is presented. In our approach, the mutation operator consists of a hybrid combination of Gaussian mutation for the continuous parameters, and Poisson mutation for the discrete parameters. The implementation uses self-adaptive schemes for updating the standard deviation of the Gaussian distribution and the mean of the Poisson distribution during the evolution. As an example, the proposed evolutionary algorithm is applied to the constraint designs of various multilayer dielectric-filter structures.