Antenna optimization using an evolutionary programming algorithm with a hybrid mutation operator

Conventional implementation of evolutionary programming (EP) for continuous parameter optimization uses Gaussian mutations. However, an implementation of EP with Cauchy mutation operator has empirically been shown to outperform EP using the Gaussian mutations for optimizations of multi-modal functions with many local optima. The faster convergence of EP with Cauchy mutation has been explained in terms of the fatter tails of the Cauchy probability density function, which results in a higher probability of escaping from a local optimum. In this work we present an implementation of EP consisting of a hybrid linear combination of the Cauchy and Gaussian mutations for antenna optimization problems in order to exploit the desirable properties of these two operators. The implementation follows a procedure similar to the one described by Chellapilla (see IEEE Trans. on Evolutionary Computation, September 1998) for function optimization and uses a self-adaptive scheme for updating the standard deviation and the scale parameter of Gaussian and Cauchy distributions, respectively, during the evolution. As an example the optimization of a six-element Yagi-Uda array of dipoles using the hybrid method is presented.