  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.
