用树数搜索方法改进遗传规划的进化性能

Takashi Ito
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引用次数: 0

摘要

在进化中,遗传规划(GP)被用来获得目标问题的合适的动作规则。因为动作规则是用树状结构表示的,所以它们的含义很容易理解。此外,提出了具有多树结构的GP用于智能体学习,并提出了一种方法来确定进化过程中每个目标问题必须设置为个体的多棵树的数量。在本研究中,我们重点研究了一种在进化中寻找合适数量的多树的算法,并引入了一种条件概率生成个体的方法来提高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Evolution Performance for Genetic Programming with Method to Search Numbers of Trees
In evolution, Genetic programming (GP) is proposed to obtain suitable action rules for a target problem. Because action rules are expressed in a tree structure, their meaning is easily understandable. In addition, GP with multiple tree structures has been proposed for agent learning, and a method has been proposed to decide the number of multiple trees that must be set to individuals for each target problem during evolution. In this study, we focused on an algorithm to search for the suitable number of multiple trees in evolution and introduced a method for generating individuals with conditional probability to improve performance.
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