征服排序:信息熵保持进化群体的分布特征研究

Ziyang Weng, Shuhao Wang, Jingwei Zhang
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引用次数: 0

摘要

本文考虑历史进程中不同国家和地区的客观进化目标,将复杂的军事征服与深层生存空间需求相结合,提出了一种保持信息熵进化群体的分布式表征算法。我们认为,首先,我们需要用分布深度来描述种群中个体的复杂进化目标;为避免算法过早成熟陷入局部最优解,引入空间分布机制促进区域发展;其次,提出了基于群体的群体目标歧视机制;最后,通过研究案例验证了算法的有效性。实验结果表明,快速排序有利于军事征服过程中个体目标的进化,更有利于区域个体的协同进化。本文能够更好地表征多个智能节点之间关系的复杂网络,为复杂系统的智能决策提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conquering Sorting: A Study of the Distributional Characteristics of Information Entropy-Preserving Evolutionary Groups
In this paper, we take into account the objective evolutionary goals of different countries and regions in the course of history, combine complex military conquest with deep survival space requirements, and propose a distributional characterization algorithm for information entropy preserving evolutionary groups. We believe that firstly, we need to describe the complex evolutionary goals of individuals in the population with distributional depth; to avoid the algorithm from maturing prematurely and falling into local optimal solutions, we introduce a spatial distribution mechanism to facilitate regional development; secondly, we propose a group goal discrimination mechanism based on the population; finally, we verify the effectiveness of the algorithm using research cases. The experimental results show that the fast sorting is beneficial to the evolution of individual goals in the process of military conquest, and more beneficial to the coevolution of regional individuals. This paper can better represent the complex network of relationships between multiple intelligent nodes and provide a reference for intelligent decision making of complex systems.
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