(575h) Evaluation of an MINLP Formulation for Symbolic Regression
Recently, Cozad and Sahinidis  proposed an MINLP formulation that uses different variations of the base formulation that reduce the search space based on redundancy elimination, symmetry breaking, and node priorities. The purpose of the current paper is to gain insights into the formulation in  by evaluating it on a large number of problems. To improve our understanding of the formulationâs capabilities, we evaluated the eight different versions of the model against a benchmark set of sampled known functional forms as well as real world datasets with unknown models at different binary expression tree depths and complexity of the determined function . This evaluation was conducted by running the models in parallel and terminating when any version of the model converged. Further work was also conducted on the bounding of the binary expression tree for the regressed values, as well as first and second derivative behaviors. Insights gained from these computations were crucial and facilitated the constrained regression of the embedded alpha function of cubic equations of state with constraints on the first and second derivatives.
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