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When creating a multi-objective optimisation/MCDM algorithm such as NSGA-ii, does it make sense to use a deep neural network trained on a supervised tabular regression prediction task, in place of a simple equation for the objective function?

Is possible or advantageous to replace a nonlinear equation with model.predict() function in Keras to be able to model more complex objective functions?

I am using pymoo with nsga-ii

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This nonlinear equation will again be approximated with the net. There is no point in introducing this much computation complexity, if it is not learned by than than it wont be learned. Ocam rasor

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  • $\begingroup$ What do you mean when you say, "if it is not learned by than than it wont be learned"? In my case, the DNN is the simplest function to represent a given criteria. A simpler non-linear equation does not exist. The DNN is given a set of variables as inputs and produces an output that does not approximate a nonlinear equation. $\endgroup$ – MachuPichu Mar 14 at 9:32

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