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wls2002
2023-07-27 23:13:43 +08:00
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@@ -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
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}.