Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (ACO). CANTS utilizes a continuous search space to indirectly-encode a neural architecture search space. Synthetic ant agents explore CANTS' continuous search space based on the density and distribution of pheromones, strongly inspired by how ants move in the real world. This continuous search space allows CANTS to automate the design of artificial neural networks (ANNs) of any size, removing a key limitation inherent to many current NAS algorithms that must operate within structures of a size that is predetermined by the user. This work expands CANTS by adding a fourth dimension to its search space representing potential neural synaptic weights. Adding this extra dimension allows CANTS agents to optimize both the architecture as well as the weights of an ANN without applying backpropagation (BP), which leads to a significant reduction in the time consumed in the optimization process: at least an average of 96% less time consumption with very competitive optimization performance, if not better. The experiments of this study - using real-world data - demonstrate that the BP-Free CANTS algorithm exhibits highly competitive performance compared to both CANTS and ANTS while requiring significantly less operation time.

翻译：连续蚁群拓扑搜索（CANTS）是一种先前提出的基于蚁群优化（ACO）的新型自然启发式神经架构搜索（NAS）算法。CANTS利用连续搜索空间间接编码神经架构搜索空间。合成蚁群代理根据信息素的密度和分布探索CANTS的连续搜索空间，其灵感强烈来源于真实世界中蚂蚁的运动方式。这种连续搜索空间使CANTS能够自动化设计任意规模的人工神经网络（ANN），消除了当前许多NAS算法必须在用户预定义大小结构内运行的关键限制。本研究通过向搜索空间添加代表潜在神经突触权重的第四维度来扩展CANTS。添加这一额外维度使CANTS代理无需应用反向传播（BP）即可同时优化ANN的架构和权重，从而显著减少优化过程所消耗的时间：平均至少减少96%的时间消耗，同时保持极具竞争力的优化性能甚至更优。本实验使用真实世界数据证明，无BP的CANTS算法在运行时间显著减少的情况下，与CANTS和ANTS相比展现出高度竞争的性能表现。