Among the wide variety of evolutionary computing models, Finite State Machines (FSMs) have several attractions for fundamental research. They are easy to understand in concept and can be visualised clearly in simple cases. They have a ready fitness criterion through their relationship with Regular Languages. They have also been shown to be tractably evolvable, even up to exhibiting evidence of open-ended evolution in specific scenarios. In addition to theoretical attraction, they also have industrial applications, as a paradigm of both automated and user-initiated control. Improving the understanding of the factors affecting FSM evolution has relevance to both computer science and practical optimisation of control. We investigate an evolutionary scenario of FSMs adapting to recognise one of a family of Regular Languages by categorising positive and negative samples, while also being under a counteracting selection pressure that favours fewer states. The results appear to indicate that longer strings provided as samples reduce the speed of fitness gain, when fitness is measured against a fixed number of sample strings. We draw the inference that additional information from longer strings is not sufficient to compensate for sparser coverage of the combinatorial space of positive and negative sample strings.

翻译：在众多进化计算模型中，有限状态机（FSM）因其在基础研究中的若干优势而备受关注。其概念易于理解，在简单情形下可清晰可视化；通过与正则语言的关系，可建立现成的适应度准则；且已被证明具有可处理的进化能力，甚至在特定场景中展现出开放进化迹象。除理论吸引力外，FSM作为自动控制与用户触发控制的典范，在工业应用中也具有重要价值。加深对FSM进化影响因素的理解，对计算机科学及控制优化实践均具有现实意义。本文研究了FSM在正向与负向样本分类中适应某一正则语言家族的进化场景，同时施加了倾向于更少状态的逆向选择压力。结果表明：当适应度依据固定数量的样本字符串进行衡量时，作为样本提供的长字符串会降低适应度增益速度。我们推断，长字符串提供的额外信息不足以补偿正负样本字符串组合空间覆盖率的稀疏性。