16 September 1992 Preliminary report on machine learning via multiobjective optimization
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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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ziauddin Ahmad, Ziauddin Ahmad, Allon Guez, Allon Guez, } "Preliminary report on machine learning via multiobjective optimization", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140027; https://doi.org/10.1117/12.140027

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