Fast Genetic Algorithm for feature selection — A qualitative approximation approach
作者：
Highlights：
• A method to construct computationally efficient meta-models for feature selection.
• A definition of the necessary conditions to ensure correctness of EA computation.
• Our algorithm converges faster than a wrapper GA, particularly for large datasets.
• The approach is useful independently of the used metaheuristic as shown with PSO.
• We demonstrate the HUX crossover averaging effect when used in binary optimization.
摘要
•A method to construct computationally efficient meta-models for feature selection.•A definition of the necessary conditions to ensure correctness of EA computation.•Our algorithm converges faster than a wrapper GA, particularly for large datasets.•The approach is useful independently of the used metaheuristic as shown with PSO.•We demonstrate the HUX crossover averaging effect when used in binary optimization.
论文关键词：Feature selection,Evolutionary computation,Genetic Algorithm,Particle Swarm Intelligence,Fitness approximation,Meta-model,Optimization
论文评审过程：Received 19 November 2021, Revised 10 August 2022, Accepted 10 August 2022, Available online 18 August 2022, Version of Record 5 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118528
