发表机构
Information Management Institute（信息管理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究采用TPE优化ES-HyperNEAT的超参数，在MNIST任务上取得优于随机搜索的效果，发现优化后的超参数可迁移至Fashion-MNIST但在简单逻辑运算上迁移有限，为神经演化算法超参数的选择与跨任务迁移提供了参考。
AI 中文摘要
增强拓扑神经演化算法（NEAT）及其高级版本可演化基质HyperNEAT（ES-HyperNEAT）在开发神经网络方面展现出巨大潜力，但其效果高度依赖超参数的选择。本研究在MNIST分类任务上采用树结构Parzen估计器（TPE）对ES-HyperNEAT的超参数进行优化，探索了超过30亿种潜在组合的搜索空间。TPE能有效遍历这个庞大空间，在平均、中位数及最佳准确率方面显著优于随机搜索。验证过程中，TPE找到的最佳超参数配置在MNIST上达到29.00%的准确率，超越了以往研究，同时使用了更小的种群规模和更少的迭代代数。还探究了优化后超参数在逻辑运算和Fashion-MNIST任务中的可迁移性，发现其可成功迁移至更复杂的Fashion-MNIST问题，但在较简单的逻辑运算上迁移效果有限。本研究提出了一种释放神经演化算法全部潜力的方法，并为超参数在不同复杂度任务间的可迁移性提供了见解。
英文摘要
Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-HyperNEAT), have shown great potential in developing neural networks. However, their effectiveness heavily depends on the selection of hyperparameters. This study investigates the optimization of ES-HyperNEAT hyperparameters using the Tree-structured Parzen Estimator (TPE) on the MNIST classification task, exploring a search space of over 3 billion potential combinations. TPE effectively navigates this vast space, significantly outperforming random search in terms of mean, median, and best accuracy. During the validation process, the best hyperparameter configuration found by TPE achieves an accuracy of 29.00% on MNIST, surpassing previous studies while using a smaller population size and fewer generations. The transferability of the optimized hyperparameters is explored in logic operations and Fashion-MNIST tasks, revealing successful transfer to the more complex Fashion-MNIST problem but limited to simpler logic operations. This study emphasizes a method to unlock the full potential of neuroevolutionary algorithms and provides insights into the hyperparameters' transferability across tasks of varying complexity.
CommentsPages 1879 - 1887
Journal refGECCO 2024 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
