paper

Author: Freakwill

paper/paper.md

@@ -37,7 +37,7 @@ Leveraging the principles of object-oriented programming (OOP) and the meta-prog
 
 # Statement of need
 
-As one of the earliest developed optimization algorithms [@holland; @katoch], the genetic algorithm (GA) has found extensive application across various domains and has undergone modifications and integrations with new algorithms [@alam; @cheng; @katoch]. The principles of GA will not be reviewed in this article. For a detailed understanding, please refer to references [@holland; @simon] and the associated literatures.
+As one of the earliest developed optimization algorithms [@holland; @katoch], the genetic algorithm (GA) has found extensive application across various domains and has undergone modifications and integrations with new algorithms [@alam; @cheng; @katoch]. The principles of GA will not be reviewed here. For details, refer to the references [@holland; @simon].
 
 In a typical Python implementation, populations are defined as lists of individuals, with each individual represented by a chromosome composed of a list of genes. Creating an individual can be achieved utilizing either the standard library's `array` or the widely-used third-party library [`numpy`](https://numpy.org/) [@numpy]. The evolutionary operators are defined on these structures.