The real-biased multiobjective genetic algorithm and its application to the design of wire antennas

This work presents a multiobjective genetic algorithm with a novel feature, the real biased crossover operator. This operator takes into account the function values of the two parents, defining a nonuniform probability for the new individuals´ locations that biases them toward the best parents´ locations. The procedure leads to better estimates of the Pareto set. The proposed algorithm is applied to the optimization of a Yagi-Uda antenna in a wide frequency range with several simultaneous performance specifications, providing antenna geometries with good performance, compared to those presented in the available literature.