ArticleiScience2023
An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Adaptive memory-based opposition and midpoint mutation in black winged kite algorithm for global optimization and engineering applications.Scientific reports · 2026Article
- Chaotic Lévy and adaptive restart enhance the Manta Ray foraging optimizer for gene feature selection.Scientific reports · 2025Article
- An enhanced machine learning-based prognostic prediction model for patients with AECOPD on invasive mechanical ventilation.iScience · 2024Article
- An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems.Biomimetics (Basel, Switzerland) · 2023Article
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8 authors.
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Abstract
The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.
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