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肝癌算法:一个新的生物灵感优化器.

Essam H Houssein1, Diego Oliva2, Nagwan Abdel Samee3

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt.

Computers in biology and medicine
|September 7, 2023
PubMed
概括
此摘要是机器生成的。

一个新的肝癌算法 (LCA) 模仿瘤生长以优化. 它在基准功能方面表现出竞争力,在生物医学特征选择方面表现出色,在MAO数据集上达到98.7%的准确性.

关键词:
生物启发的生物灵感.功能选择 (FS) 功能选择肝癌算法 (LCA) 是一种算法.超启发式算法 (MAs) 是一种算法.优化优化 优化优化基于随机对立的学习 (ROBL)

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科学领域:

  • 计算智能是一种计算智能.
  • 生物启发的计算 生物启发的计算
  • 优化算法 优化算法

背景情况:

  • 优化算法对于解决复杂的计算问题至关重要.
  • 现有的算法往往难以平衡本地和全球搜索功能.
  • 生物启发的方法为提高优化性能提供了新的策略.

研究的目的:

  • 介绍一个新的生物灵感优化算法,肝癌算法 (LCA).
  • 评估LCA在标准基准函数上的表现,并将其与现有的元启发算法进行比较.
  • 为了证明LCA对现实世界问题的适用性,在生物医学数据分类中具体选择特征.

主要方法:

  • 开发了肝癌算法 (LCA),模拟了肝脏瘤的生长和接管动态.
  • LCA 结合了基因操作员和基于随机对立的学习 (ROBL) 策略,以有效地探索太空.
  • 该算法与CEC'2020测试套件中的七个已建立的元启发算法进行了基准测试.

主要成果:

  • 在CEC'2020基准函数上,LCA算法表现出与已建立的算法相比具有竞争力的性能.
  • 通过将LCA与支持矢量机组结合在一起的LCA-SVM模型,在生物医学数据分类中实现了高精度.
  • 具体来说,LCA-SVM模型在单胺氧化酶 (MAO) 数据集上获得了98.704%的准确性.

结论:

  • 肝癌算法 (LCA) 是一种有效的生物灵感优化技术.
  • 在数学优化和实际特征选择任务中,LCA表现出卓越的性能.
  • LCA-SVM模型为准确的生物医学数据分类提供了一个有希望的方法.