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 individualsʹ locations that biases them toward the best parentsʹ 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.