The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus on representation and search algorithms, as they have drawn significant interest from the feature selection community due to their potential for global search and simplicity. An analysis of various advanced approach types, along with their advantages and disadvantages, is presented in this study, with the goal of highlighting important issues and unanswered questions in the literature. The article provides advice for conducting future research more effectively to benefit this field of study, including guidance on identifying appropriate approaches to use in different scenarios.

特征选择的主要目标是通过选择简洁且信息丰富的特征子集来提高学习性能，由于涉及的搜索空间大而复杂，这对机器学习或模式识别应用提出了一项具有挑战性的任务。本文对特征选择问题的自然启发式元启发式方法进行了深入研究，重点关注表示和搜索算法，因为它们具有全局搜索和简单性的潜力，引起了特征选择社区的极大兴趣。本研究分析了各种高级方法类型及其优缺点，目的是突出文献中的重要问题和未解决的问题。