论文标题
在可变环境中改善整洁的适应性
Adaptability of Improved NEAT in Variable Environments
论文作者
论文摘要
人工智能（AI）的巨大挑战是训练控制剂，可以适当适应可变环境。条件发生变化的环境可能会导致试图在其中操作的代理商的问题。因此，构建可以训练代理在这些环境中运行并正确处理不断变化的条件的算法很重要。增强拓扑的神经进化（整洁）是一种新型的遗传算法（GA），但由于较新的气体效果超过了它，但它却掉落了。本文通过在可变环境中实施各种版本的整洁，以确定整洁是否可以在这些环境中表现良好，从而进一步发展了这一主题的研究。在每种组合中，包括：反复连接，自动特征选择和人口规模增加。复发连接的改进表现非常好。发现自动选择的改进被发现对性能有害，并且人口规模的提高降低了少量的绩效，但计算要求降低了明显降低。
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.
