一个基于双种群的多目标进化算法,以统一的样本为神经架构搜索神经架构
IEEE transactions on neural networks and learning systems
|February 10, 2026
概括
本研究介绍了MOEA-BUS,这是一个用于神经架构搜索 (NAS) 的新算法,可以优化准确性和网络复杂性. 它增强了人口多样性和搜索空间覆盖范围,以获得卓越的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 神经架构搜索 (NAS) 自动化了神经网络设计,但在优化诸如精度和复杂性等多个目标方面面临挑战.
- 现有的NAS方法往往缺乏人口多样性和足够的搜索空间探索,特别是在复杂的地区.
研究的目的:
- 为NAS提出一种新的多目标进化算法 (MOEA-BUS),同时优化神经网络的准确性和复杂性.
- 通过改善人口多样性和搜索空间覆盖率来提高NAS的效率和有效性.
主要方法:
- 开发了MOEA-BUS,这是一个多目标进化算法,利用双种群框架和一种新的统一采样方法来初始化人口.
- 实施两个群体之间的协同进化,以确保全面的搜索空间覆盖.
- 在CIFAR-10和ImageNet数据集上进行实验以评估性能.
主要成果:
- MOEA-BUS在CIFAR-10上实现了最高准确度的98.39%,在ImageNet上达到80.03%.
- 在ImageNet上实现了78.28%的准确性,网络复杂度低,为446M MAdds.
- 废除研究证实,统一的抽样和双种群机制显著改善了人口多样性和整体表现.
结论:
- MOEA-BUS有效地优化了NAS中的精度和网络复杂性,优于现有的方法.
- 拟议的统一抽样和双种群策略对于提高NAS的多样性和性能至关重要.
- 该算法显示了设计高效和高性能神经网络的巨大潜力.
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