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Found 3 papers, 1 papers with code
Learning Enhanced Optimisation for Routing Problems
However, there is still a substantial gap in solution quality between machine learning and operations research algorithms.
How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function
It also investigates the importance of loss function and error criterion in machine learning models where they are used for predicting solutions of optimization problems.
SPSA-FSR: Simultaneous Perturbation Stochastic Approximation for Feature Selection and Ranking
After a review of the current state-of-the-art, we discuss our improvements in detail and present three sets of computational experiments: (1) comparison of SPSA-FS as a (wrapper) feature selection method against sequential methods as well as genetic algorithms, (2) comparison of SPSA-FS as a feature ranking method in a classification setting against random forest importance, chi-squared, and information main methods, and (3) comparison of SPSA-FS as a feature ranking method in a regression setting against minimum redundancy maximum relevance (MRMR), RELIEF, and linear correlation methods.
