A greedy genetic algorithm for continuous variables electromagnetic optimization problems

A greedy genetic algorithm for continuous variables electromagnetic optimization problems is presented. The presented algorithm is characterized by the use of a nonlinear simplex method as a principal optimizer, and of a greedy genetic algorithm to explore the search space, realizing a balance between diversity and a bias toward fitter individuals. The resulting algorithm merges the efficiency typical of calculus-based search with the robustness typical of random methods. A detailed comparison of the performance obtained implementing several strategies is presented, using an electromagnetic design test problem.