HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

Abstract

MIDFIELD dataset is a unit-record longitudinal dataset for undergraduate students from 16 universities. MIDFIELD contains all the information that appears in students' academic records, including demographic data (sex, age, and race/ethnicity) and information about major, enrollment, graduation, and school and pre-school performance.

Stochastic Model Predictive Control With Closed-Loop Model Updating

“…Establish all the values of W 1 to be used in an increasing order. Similar to hyperparameter tuning in machine learning models, 31 a grid search methodology that starts from zero to a large value is implemented. 4.…”

Optimized convolution neural network-based model for detection and classification of pulmonary diseases

“…In addition, conventional techniques for hyperparameter optimization include Grid Search (GS) and Random Search (RS) (Bharati et al, 2021). Traditional optimization techniques are less effective in hyperparameter tuning problems compared to alternative strategies like metaheuristic algorithms (Zahedi et al, 2021, Rajagopal et al, 2023. A metaheuristic algorithm is a set of techniques used to solve complex and large-scale optimization problems, such as hyperparameter tuning in search space optimization.…”

“…A metaheuristic algorithm is a set of techniques used to solve complex and large-scale optimization problems, such as hyperparameter tuning in search space optimization. The collection of hyperparameter combinations that the Genetic Algorithm (GA) discovers to be effective in each generation is then iterated through until the combination with the best performance is identified (Alibrahim et al, 2021, Zahedi et al, 2021. To find and update the global ideal throughout each iteration until the final optimal value is obtained, the Particle Swarm Optimization (PSO) algorithm communicates with other particles (Alibrahim et al, 2021).…”