神经网络拓扑结构的多准则演化：自治系统中经验与性能的平衡
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
摘要：
自治系统中的大多数人工神经网络（ANN）实现使用固定/用户指定的网络拓扑，导致次优性能和低可移植性。现有的增强拓扑或NEAT范式的神经进化提供了一个强大的替代方案，允许网络拓扑和连接权重通过进化过程同时优化。然而，大多数NEAT实现只允许考虑单个目标。还有一个问题是如何可追踪地引入拓扑多样化，以减轻对训练场景的过度拟合。为了解决这些差距，本文开发了一种多目标神经进化算法。在采用NEAT的基本要素的同时，对选择、物种形成和突变过程进行了重要修改。以小型机器人路径规划应用为背景，提出了一种经验增益准则，以封装系统所遇到的各种局部环境。这个标准促进了支持探索的基因的进化，从而寻求从比单独使用性能最大化可能的更小的任务场景集合中进行概括。单目标（优化性能）和多目标（优化性能和经验增益）的神经进化方法的有效性进行了评估，在两个不同的小型机器人的情况下，与人工神经网络获得的多目标优化观察到在看不见的情况下提供卓越的性能。
英文摘要：
Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
