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Corrections to ‘An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection
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Keywords
Opposition-based optimization
Great Wall Construction
Gaussian mutation
Feature selection
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Abstract
Feature selection is a fundamental problem in machine learning and data-driven optimization, directly influencing model efficiency, accuracy, and computational cost. Traditional metaheuristic algorithms often struggle with premature convergence and local optima entrapment, particularly when handling high-dimensional datasets. This study investigates corrections to the Opposition-Based Great Wall Construction (OBGWC) metaheuristic algorithm augmented with Gaussian mutation, focusing on optimizing feature selection processes. The proposed corrections aim to enhance global search capabilities while preserving computational efficiency. Through systematic theoretical analysis and empirical evaluation, the study demonstrates improved convergence rates, higher-quality feature subsets, and reduced susceptibility to local optima. The findings indicate that integrating opposition-based strategies with Gaussian perturbations effectively balances exploration and exploitation, providing a robust framework for high-dimensional feature selection tasks. Implications for machine learning practitioners and intelligent system designers are discussed.
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