Exploring Bio-inspired Optimization for Hyperparameter Tuning in Deep Learning Image Classification

doi:10.1007/978-981-96-6294-4_6

2026-01-01

PRE

P. J. Hsu

Wang, Yi-Syuan

Mu‐Yen Chen *

DOI：10.1007/978-981-96-6294-4_6

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模型参数优化是人工智能（AI）中的关键挑战，对模型性能和效率产生深远影响。传统方法如网格搜索和手动调参往往耗时且劳动密集。本研究探讨了仿生优化算法在图像分类模型超参数调优中的有效性。研究根据其解更新机制将算法分为三类：基于序列、基于向量和基于映射的方法。我们设计了实验以探究分类类别数量变化对仿生算法性能及评估的影响。结果表明，基于序列的算法在类别数量极少和极多的数据集上表现出更好的适应性和更高的准确性。基于映射的算法在CIFAR-10数据集上达到了最高准确率。相比之下，基于向量的仿生算法在所有数据集上均保持中等性能。本研究为仿生算法的超参数调优提供了全面评估，揭示了其优缺点。

Keyword:

Bio-Inspired Algorithms

Ant Colony Optimization

Genetic Algorithm

Hyperparameter Optimization

Image Classification Models

期刊

UBI-MEDIA COMPUTING, PERVASIVE SYSTEMS, ALGORITHMS AND NETWORKS, UBI-MEDIA 2025, I-SPAN 2025, PART I

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national cheng kung university

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