18 citations · 32 across the 14 of their papers we have counts for
4 papers · 1 filter
A Hierarchical Importance-Guided Multi-objective Evolutionary Framework for Deep Neural Network Pruning
Zak Khan, Azam Asilian Bidgoli
The optimization of over-parameterized deep neural networks represents a large-scale, high-dimensional, and strongly non-convex decision problem that challenges existing optimizati…
Large-scale Multi-objective Feature Selection: A Multi-phase Search Space Shrinking Approach
Azam Asilian Bidgoli, Shahryar Rahnamayan
Feature selection is a crucial step in machine learning, especially for high-dimensional datasets, where irrelevant and redundant features can degrade model performance and increas…
Image-Based Benchmarking and Visualization for Large-Scale Global Optimization
Kyle Robert Harrison, Azam Asilian Bidgoli, Shahryar Rahnamayan +1
In the context of optimization, visualization techniques can be useful for understanding the behaviour of optimization algorithms and can even provide a means to facilitate human i…
Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm
Shahryar Rahnamayan, Seyed Jalaleddin Mousavirad
Many real-world problems are categorized as large-scale problems, and metaheuristic algorithms as an alternative method to solve large-scale problem; they need the evaluation of ma…