Neuro-evolutionary methods have proven effective in addressing a wide range of tasks. However, the study of the robustness and generalisability of evolved artificial neural networks (ANNs) has remained limited. This has immense implications in the fields like robotics where such controllers are used in control tasks. Unexpected morphological or environmental changes during operation can risk failure if the ANN controllers are unable to handle these changes. This paper proposes an algorithm that aims to enhance the robustness and generalisability of the controllers. This is achieved by introducing morphological variations during the evolutionary process. As a results, it is possible to discover generalist controllers that can handle a wide range of morphological variations sufficiently without the need of the information regarding their morphologies or adaptation of their parameters. We perform an extensive experimental analysis on simulation that demonstrates the trade-off between specialist and generalist controllers. The results show that generalists are able to control a range of morphological variations with a cost of underperforming on a specific morphology relative to a specialist. This research contributes to the field by addressing the limited understanding of robustness and generalisability in neuro-evolutionary methods and proposes a method by which to improve these properties.

翻译：神经进化方法已被证明在解决广泛任务中有效，然而，关于进化人工神经网络（ANNs）鲁棒性与泛化能力的研究仍十分有限。这一现状在机器人等控制领域具有重要意义——当ANN控制器无法应对运行过程中突发的形态或环境变化时，可能导致系统失效。本文提出一种旨在增强控制器鲁棒性与泛化能力的算法，其核心策略是在进化过程中引入形态变化。由此，无需获取形态信息或调整参数，即可发现能够充分适应多种形态变化的通用控制器。我们通过大量仿真实验分析了专化型控制器与通用型控制器之间的权衡关系。结果表明：通用型控制器虽能在特定形态任务上牺牲部分性能，但可有效控制多样化的形态变化。本研究通过填补神经进化方法中鲁棒性与泛化能力认知的空白，并提出相应的改进方法，为该领域做出了贡献。