Genetic Algorithm (GA) is a popular meta-heuristic evolutionary algorithm that uses stochastic operators to find optimal solution and has proved its effectiveness in solving many complex optimization problems (such as classification, optimization, and scheduling). However, despite its performance, popularity and simplicity, not much attention has been paid towards reproducibility and reusability of GA. In this paper, we have extended Findable, Accessible, Interoperable and Reusable (FAIR) data principles to enable the reproducibility and reusability of algorithms. We have chosen GA as a usecase to the demonstrate the applicability of the proposed principles. Also we have presented an overview of methodological developments and variants of GA that makes it challenging to reproduce or even find the right source. Additionally, to enable FAIR algorithms, we propose a vocabulary (i.e. $evo$) using light weight RDF format, facilitating the reproducibility. Given the stochastic nature of GAs, this work can be extended to numerous Optimization and machine learning algorithms/methods.

翻译：遗传算法（GA）是一种流行的元启发式进化算法，它利用随机算子寻找最优解，并在解决许多复杂优化问题（如分类、优化和调度）中证明了其有效性。然而，尽管GA性能优异、应用广泛且原理简单，但其可复现性和可重用性并未受到足够重视。本文扩展了可查找、可访问、可互操作和可重用（FAIR）数据原则，以实现算法的可复现性和可重用性。我们选择GA作为用例，以展示所提原则的适用性。同时，本文概述了GA的方法学发展及其变体，这些变体使得复现甚至定位正确来源变得困难。此外，为了实现FAIR算法，我们提出了一种使用轻量级RDF格式的词汇表（即$evo$），以促进可复现性。鉴于GA的随机性本质，本工作可推广到众多优化和机器学习算法/方法。