# Using DNN as the objective function for a multi-objective optimization algorithm

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