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  2. 交叉表示遗传编程:基于树和线性表示的案例研究.
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  2. 交叉表示遗传编程:基于树和线性表示的案例研究.

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交叉表示遗传编程:基于树和线性表示的案例研究.

Zhixing Huang1, Yi Mei2, Fangfang Zhang3

  • 1Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, 6140, New Zealand zhixing.huang@ecs.vuw.ac.nz.

Evolutionary computation
|May 14, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

这项研究引入了一种新的交叉表示遗传编程 (GP) 算法,该算法同时使用基于树的和线性表示来演变程序. 这种方法增强了GP的实力.

关键词:
交叉代表是指交叉代表.动态的工作车间安排.线性遗传编程是一种线性遗传编程.象征性回归是一种象征性回归.基于树的基因编程.

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科学领域:

  • 人工智能的人工智能
  • 计算机科学 计算机科学
  • 进化计算是一种进化计算.

背景情况:

  • 遗传编程 (GP) 方法往往依赖于特定的表示 (例如,基于树的,线性),每个都有取决于域的优点和缺点.
  • 医生代表和健身景观之间的关系是复杂的,这使得很难选择一个给定的问题的最佳代表.
  • 具有多个表示的同时演变的程序允许探索多样化的搜索空间和潜在的协同效益.

研究的目的:

  • 解决多个GP表示的同时演变的研究上的差距.
  • 提出一种新的交叉表示GP算法,利用树基和线性表示.
  • 调查跨代表性知识转移在改善GP绩效方面的有效性.

主要方法:

  • 开发一种交叉表示遗传编程 (GP) 算法.
  • 在GP框架内整合基于树和线性表示.
  • 引入一个新的交叉表示交叉运营商,旨在利用不同表示之间的相互作用.

主要成果:

  • 经验证据表明,与单一代表性GP相比,拟议的交叉代表性GP方法提高了性能.
  • 在基于树的和线性表示之间的学习知识导航提高了符号回归任务的有效性.
  • 该算法在动态工作车间调度问题上表现出更高的效率.

结论:

  • 多个GP表示的同时演变,特别是基于树的和线性的,可以导致优越的搜索能力.
  • 开发的交叉代表交叉运营商有效地利用不同代表之间的协同作用.
  • 这种方法为推进GP在各种问题领域的有效性提供了一个有希望的方向.