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Found 3 papers, 1 papers with code
Fast Genetic Algorithm for feature selection -- A qualitative approximation approach
In this paper, we propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets.
Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks
This paper investigates how various randomization techniques impact Deep Neural Networks (DNNs).
Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection
Such a framework allows for using wrapper approaches in a much more computationally efficient way, significantly increasing the quality of feature selection solutions achievable, especially on large datasets.
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