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The Algorithm Configuration Problem
The field of algorithmic optimization has significantly advanced with the development of methods for the automatic configuration of algorithmic parameters.
Learning to Configure Mathematical Programming Solvers by Mathematical Programming
We discuss the issue of finding a good mathematical programming solver configuration for a particular instance of a given problem, and we propose a two-phase approach to solve it.
A learning-based mathematical programming formulation for the automatic configuration of optimization solvers
We propose a methodology, based on machine learning and optimization, for selecting a solver configuration for a given instance.
Globally Optimal Symbolic Regression
In this study we introduce a new technique for symbolic regression that guarantees global optimality.
