diff --git a/paper.txt b/paper.txt
index 2b9166b..ae0b224 100644
--- a/paper.txt
+++ b/paper.txt
@@ -24,4 +24,14 @@ minutes. These results demonstrate the potential of NEATAX
 as a scalable and efficient solution for neuroevolution tasks,
 paving the way for the future application of NEAT in more
 complex and demanding scenarios. NEATAX is available at
-https://github.com/WLS2002/neatax
\ No newline at end of file
+https://github.com/WLS2002/neatax
+
+\section{Introduction}
+
+Inspired by the principles of natural selection and genetic inheritance, Evolutionary Computation (EC) has emerged
+as a powerful approach in the field of Artificial Intelligence (AI). EC exhibits a robust ability to explore vast
+and complex solution spaces, which is particularly critical when tackling ``black box" optimization problems where the
+internal structure isn't fully visible or understood. Leveraging the power of population-based search, EC navigates
+these complexities to arrive at near-optimal solutions \cite{eiben2015introduction}. However, despite these strengths, recent scholarship has
+highlighted limitations of EC. Important aspects such as ``openendedness" and ``genotype-to-phenotype mappings" warrant
+further attention, especially in light of EC's tendency to rely on small populations and strong selection pressure \cite{miikkulainen_biological_2021}.