Preliminary report on machine learning via multiobjective optimization

We believe that an essential feature in machine learning is the real time satisfaction of multiple objectives such as identification, tracking, etc. The machine learning problem may be viewed as a nonlinear adaptive control problem where the environment plays the role of the `plant,' while the learner is the controller. Multiobjective optimization (MOO) in the control problem typically deals with simultaneous optimization of more than one objective, where each objective is described via a cost functional. In such a situation there often exists a region of tradeoff wherein one cost may be improved at the expense of others. Such a region is called the Pareto optimal (PO) set. A parameterization of this set simplifies the attainment of the existing tradeoff. Working within the Pareto set guaranties optimum tradeoff. As an example this algorithm is applied to the control of a dc motor.