  The performance of a Multiobjective Evolutionary Algorithm (MOEA) is
crucially dependent on the parameter setting of the operators. The most desired
control of such parameters presents the characteristic of adaptiveness, i.e.,
the capacity of changing the value of the parameter, in distinct stages of the
evolutionary process, using feedbacks from the search for determining the
direction and/or magnitude of changing. Given the great popularity of the
algorithm NSGA-II, the objective of this research is to create adaptive
controls for each parameter existing in this MOEA. With these controls, we
expect to improve even more the performance of the algorithm.
  In this work, we propose an adaptive mutation operator that has an adaptive
control which uses information about the diversity of candidate solutions for
controlling the magnitude of the mutation. A number of experiments considering
different problems suggest that this mutation operator improves the ability of
the NSGA-II for reaching the Pareto optimal Front and for getting a better
diversity among the final solutions.
