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.

翻译：人工智能领域的一大挑战在于训练能够自适应可变环境的控制智能体。环境条件的变化可能导致智能体在运行过程中出现问题，因此构建能够训练智能体适应此类环境并妥善应对条件变化的算法至关重要。拓扑增强神经进化算法（NEAT）问世时曾是一种新颖的遗传算法（GA），但随着性能更优的新型遗传算法的出现已逐渐式微。本文通过将多种改进型NEAT算法部署于可变环境中进行实验，以探究NEAT在此类环境中的表现能力。所采用的改进措施包括（进行全组合实验）：递归连接、自动特征选择及种群规模递增。实验结果表明，递归连接改进表现极为优异；自动特征选择改进对性能具有负面影响；而种群规模递增改进虽使性能小幅下降，但显著降低了计算开销。