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. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research.

翻译：本文对偏随机密钥遗传算法（BRKGA）进行了全面的文献综述。BRKGA是一种元启发式算法，其在遗传算法框架中使用基于随机密钥的染色体，并采用有偏、均匀和精英交配策略。本综述涵盖了150余篇论文，涉及广泛的应用领域，包括经典组合优化问题、实际工业应用案例，以及机器学习中神经网络超参数调优等非传统应用。在所有应用领域中，调度问题最为常见，其次是网络设计和选址问题。最常用的混合方法是局部搜索，而新特征旨在提高种群多样性。总体而言，本综述全面概述了BRKGA元启发式算法及其应用，并指出了未来研究的重要方向。