This paper is a comprehensive literature review of Biased Random-Key Genetic Algorithms (BRKGA). BRKGA is a metaheuristic that employs random-key-based chromosomes with biased, uniform, and elitist mating strategies in a genetic algorithm framework. The review encompasses over 150 papers with a wide range of applications, including classical combinatorial optimization problems, real-world industrial use cases, and non-orthodox applications such as neural network hyperparameter tuning in machine learning. Scheduling is by far the most prevalent application area in this review, followed by network design and location problems. The most frequent hybridization method employed is local search, and new features aim to increase population diversity. We also detail challenges and future directions for this method. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research.

中文翻译：

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有偏差的随机密钥遗传算法：回顾

本文是对有偏随机密钥遗传算法（BRKGA）的全面文献综述。 BRKGA 是一种元启发式算法，在遗传算法框架中采用基于随机密钥的染色体，并采用有偏见的、统一的和精英主义的交配策略。该评论涵盖 150 多篇具有广泛应用的论文，包括经典的组合优化问题、现实世界的工业用例以及机器学习中的神经网络超参数调整等非正统应用。调度是本次审查中迄今为止最普遍的应用领域，其次是网络设计和位置问题。最常用的杂交方法是局部搜索，新功能旨在增加种群多样性。我们还详细介绍了这种方法的挑战和未来方向。总体而言，本次调查全面概述了 BRKGA 元启发法及其应用，并强调了未来研究的重要领域。