混沌莱维和自适应重启增强了Manta Ray寻优化器,用于基因特征选择选择
Shamsuddeen Adamu1,2, Hitham Alhussian3, Said Jadid Abdulkadir3
1Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia. shamsu200@yahoo.com.
Scientific reports
|November 25, 2025
概括
本研究介绍了CLA-MRFO,这是一种适应性优化算法,可以改善高维问题的勘探-开发平衡. 它在基准功能上表现出卓越的性能,并识别出改善诊断的关键白血病基因.
科学领域:
- 计算智能是一种计算智能.
- 生物信息学是一种生物信息学.
- 优化算法 优化算法
背景情况:
- 基于集群的优化算法在高维度中与探索-开发平衡作斗争.
- 曼塔射线食优化 (MRFO) 面临由于静态参数和过早收的局限性.
研究的目的:
- 通过引入适应性机制来提高搜索动态来增强MRFO.
- 评估拟议的CLA-MRFO在基准功能和现实世界生物信息学任务方面的表现.
主要方法:
- 通过整合混乱的莱维飞行调制,相位感知内存和基于的重启策略,开发了CLA-MRFO.
- 在CEC'17基准套件上验证了CLA-MRFO,并将其应用于高维白血病基因选择.
主要成果:
- 在29个CEC'17函数中,CLA-MRFO在23个函数中实现了最低的平均误差,其性能比其他算法高31.7%.
- 通过嵌套交叉验证,确定了针对白血病的超紧,生物学相关的基因子集,通过嵌套交叉验证实现了[公式:参见文本]的平均F1分数.
- 在多次运行中表现出一致的性能 (<5%的差异).
结论:
- CLA-MRFO为高维优化提供了适应性和有效的框架,特别是在生物信息学领域.
- 虽然对二进制分类有效,但由于上下文依赖的生物标志物识别,其对多类问题的概括性需要进一步调查.
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