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2023-07-27 23:13:43 +08:00
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@@ -25,3 +25,13 @@ as a scalable and efficient solution for neuroevolution tasks,
paving the way for the future application of NEAT in more paving the way for the future application of NEAT in more
complex and demanding scenarios. NEATAX is available at 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}.