Abstract

A popular approach to electromagnetic design is based on generic optimization algorithms which mimic natural phenomena. These algorithms have been proved feasible through multiple experiments even though there is no standard procedure to their design for a particular problem. Hybrid schemes allow an insightful and quite efficient approach towards their design. Furthermore, an intrinsic characteristic of this class of algorithms is their randomness, which is precisely the base the no-free-lunch theorem exploits to measure their fit to a particular problem. This characterization permits a reproducible comparison between algorithms through their fitness density functions.