Fog computing is a new computational paradigm that emerged from the need to reduce network usage and latency in the Internet of Things (IoT). Fog can be considered as a continuum between the cloud layer and IoT users that allows the execution of applications or storage/processing of data in network infrastructure devices. The heterogeneity and wider distribution of fog devices are the key differences between cloud and fog infrastructure. Genetic-based optimization is commonly used in distributed systems; however, the differentiating features of fog computing require new designs, studies, and experimentation. The growing research in the field of genetic-based fog resource optimization and the lack of previous analysis in this field have encouraged us to present a comprehensive, exhaustive, and systematic review of the most recent research works. Resource optimization techniques in fog were examined and analyzed, with special emphasis on genetic-based solutions and their characteristics and design alternatives. We defined a classification of the optimization scope in fog infrastructures and used this optimization taxonomy to classify the 70 papers in this survey. Subsequently, the papers were assessed in terms of genetic optimization design. Finally, the benefits and limitations of each surveyed work are outlined in this paper. Based on these previous analyses of the relevant literature, future research directions were identified. We concluded that more research efforts are needed to address the current challenges in data management, workflow scheduling, and service placement. Additionally, there is still room for improved designs and deployments of parallel and hybrid genetic algorithms that leverage, and adapt to, the heterogeneity and distributed features of fog domains.

翻译：雾计算是一种新的计算模式,它产生于减少网络使用和静态在物联网互联网(IoT)中的需要。 雾计算可被视为云层和IoT用户之间的一个连续体,使得可以在网络基础设施设备中应用或储存/处理数据。雾装置的异质性和广泛分布是云与雾基础设施之间的主要差异。基于遗传的优化通常用于分布式系统;但是,雾计算的不同特征需要新的设计、研究和实验。基因雾资源优化领域的研究日益增多,以及该领域先前分析的缺乏,鼓励我们提出对最新研究工作的全面、详尽和系统审查。对雾中资源优化技术进行了研究和分析,特别强调基于基因的解决办法及其特点和设计替代办法。我们界定了雾基础设施的最优化范围的分类,并使用这种最优化分类法对本次调查中的70份文件进行分类。随后,从基因优化设计的角度对论文进行了评估。最后,每份受调查的云层资源优化研究的效益和局限性在本文中概述。根据先前的研究结果,我们完成了对当前管理流程的定位,因此,在以往的排序方面,我们完成了有关研究的排序工作,因此,在目前的工作流程中完成了有关研究。