From 86d085abcdbe46b270e8b3a401ef504033835c08 Mon Sep 17 00:00:00 2001 From: wls2002 Date: Thu, 27 Jul 2023 23:13:43 +0800 Subject: [PATCH] add paper.txt --- paper.txt | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) 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}.