A Linear Programming Enhanced Genetic Algorithm for Hyperparameter Tuning in Machine Learning
This addresses the problem of efficient hyperparameter tuning for machine learning practitioners, but it is incremental as it builds on existing population-based methods.
The paper tackles hyperparameter tuning in machine learning by formulating it as a bilevel program and solving it with a micro genetic algorithm enhanced by a linear program for continuous hyperparameters, showing significant promise on MNIST and CIFAR-10 datasets.
In this paper, we formulate the hyperparameter tuning problem in machine learning as a bilevel program. The bilevel program is solved using a micro genetic algorithm that is enhanced with a linear program. While the genetic algorithm searches over discrete hyperparameters, the linear program enhancement allows hyper local search over continuous hyperparameters. The major contribution in this paper is the formulation of a linear program that supports fast search over continuous hyperparameters, and can be integrated with any hyperparameter search technique. It can also be applied directly on any trained machine learning or deep learning model for the purpose of fine-tuning. We test the performance of the proposed approach on two datasets, MNIST and CIFAR-10. Our results clearly demonstrate that using the linear program enhancement offers significant promise when incorporated with any population-based approach for hyperparameter tuning.