A Linear Programming Enhanced Genetic Algorithm for Hyperparameter Tuning in Machine Learning

Abstract

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.

8 pages; https://ieeexplore.ieee.org/document/10254162/

References in corpus (7)

• Practical Bayesian Optimization of Machine Learning Algorithms
• Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
• Gradient-based Hyperparameter Optimization through Reversible Learning
• Finding Optimal Strategies in a Multi-Period Multi-Leader-Follower Stackelberg Game Using an Evolutionary Algorithm
• Hyperparameter optimization with approximate gradient
• Stochastic Hyperparameter Optimization through Hypernetworks
• A Gradient-based Bilevel Optimization Approach for Tuning Hyperparameters in Machine Learning