Application of a Hybrid Genetic and Hill Climbing Algorithm (HGA) in Metaverse-Based Learning Environments
Application of a Hybrid Genetic and Hill Climbing Algorithm (HGA) in Metaverse-Based Learning Environments
DOI:
https://doi.org/10.54153/sjpas.2026.v8i1.1359Keywords:
Application of a Hybrid Genetic and Hill Climbing Algorithm (HGA) in Metaverse-Based Learning EnvironmentsAbstract
Feature selection patterns are used to detect and remove redundant and unwanted features from the original feature vector that do not help the authentication model perform well. Meta-heuristic feature selection algorithms must strike a reasonable balance between utilisation and exploration of the search universe to solve this problem. A typical meta-heuristic algorithm, the Genetic Algorithm (GA), lacks exploitation, which limits its local search capabilities. To cope with exploitation, GA uses a mutation mechanism that has several problems. Result: GA becomes stuck in local optima. In the current work, we are using hybridisation of the Hill Climbing algorithm (HCA), a local search method, with GA to overcome this issue. Instead of using the GA's mutation technique, HCA is used in this circumstance. The application of perturbation of candidate solutions in HCA results in a high degree of exploitation. Hill Climbing and Genetic Algorithm (HGHCA) is the suggested approach. We used the HGHCA on 9 publicly available standard datasets from the Machine Learning Repository (UCI). We tested three different classifiers to demonstrate the proposed feature selection method's classifier independence: Linear Search Algorithm (LSA), Feed Forward Neural Network (FFNW), and Naive Bayes (NB).
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