基于扰动修改的遗传算法
Perturbation-Based Modified Genetic Algorithm
李俊良1
作者信息
- 1. 三峡大学 计算机与信息学院,湖北 宜昌 433000
- 折叠
摘要
文章主要探讨了系统论的扰动概念与遗传算法的融合,目标是解决一系列具有不确定性的优化问题.算法模拟了自然界的遗传机制,通过引入随机扰动来模拟不确定性,并在遗传进化过程中逐步提升解决方案的质量.在系统论中,"扰动"指的是对系统产生影响的外部因素,或者是由系统内部因素引起的偏差.这些扰动可能是随机的,也可能是周期性的.本算法展现出了高度的鲁棒性,即使在目标函数发生变化后,仍能保持良好的性能.因此,该算法可以被视为解决类似优化问题的强大工具.
Abstract
The article primarily explores the integration of the concept of perturbation in systems theory with genetic algorithms,with the aim of solving a series of optimization problems characterized by uncertainty.Algorithm simulates the genetic mechanisms found in nature,introducing random perturbations to emulate uncertainty,and progressively enhancing the quality of solutions throughout the genetic evolution process.In systems theory,"perturbation"refers to external factors that impact the system or deviations caused by internal factors of the system.These perturbations can be random or periodic.This algorithm demonstrates a high degree of robustness,maintaining good performance even when the objective function changes.Therefore,this algorithm can be considered a powerful tool for solving similar optimization problems.
关键词
进化算法/系统论/扰动Key words
evolutionary algorithm/systems theory/perturbation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据