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This paper presents systematic literature survey on meta-heuristic algorithms that provide solutions for the multiclass feature comprehension problems in machine learning. Primary behaviors such evolutionary, swarm-intelligence, physics and human-life are the phenomenal and compositional structures in meta-heuristic algorithms. The present review examines the variants of multiclass feature selection, variable classifiers and other application areas. The current article focuses on the certain challenges in meta-heuristic algorithms and identifies gaps useful for the future research studies.

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Metaheuristics and Hybrid Evolutionary Methods for Feature Selection

  • 会议论文

  • 发表日期：

    2025年

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  • Kondra Pranitha,
  • Nalajam Geethanjali,
  • E. Sreedevi,
  • A. Jyothi Babu,
  • Naresh Vurukonda,
  • K. Rajakumari

摘要

This paper presents systematic literature survey on meta-heuristic algorithms that provide solutions for the multiclass feature comprehension problems in machine learning. Primary behaviors such evolutionary, swarm-intelligence, physics and human-life are the phenomenal and compositional structures in meta-heuristic algorithms. The present review examines the variants of multiclass feature selection, variable classifiers and other application areas. The current article focuses on the certain challenges in meta-heuristic algorithms and identifies gaps useful for the future research studies.