  A large challenge in Artificial Intelligence (AI) is training control agents
that can properly adapt to variable environments. Environments in which the
conditions change can cause issues for agents trying to operate in them.
Building algorithms that can train agents to operate in these environments and
properly deal with the changing conditions is therefore important.
NeuroEvolution of Augmenting Topologies (NEAT) was a novel Genetic Algorithm
(GA) when it was created, but has fallen aside with newer GAs outperforming it.
This paper furthers the research on this subject by implementing various
versions of improved NEAT in a variable environment to determine if NEAT can
perform well in these environments. The improvements included, in every
combination, are: recurrent connections, automatic feature selection, and
increasing population size. The recurrent connections improvement performed
extremely well. The automatic feature selection improvement was found to be
detrimental to performance, and the increasing population size improvement
lowered performance a small amount, but decreased computation requirements
noticeably.
