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graph coloring相关的网络例句

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与 graph coloring 相关的网络例句 [注:此内容来源于网络,仅供参考]

The graph coloring problem is a widely studied combinatorial optimization problem.

图的着色问题是一个被广泛研究的组合优化问题。

Graph coloring problem, you can use four kinds of colors to a different places on the map with different colors, and no more than four colors.

详细说明:图的着色问题,可以使用四种颜色给一个地图上的不同地方着上不同的颜色,并且不超过四种颜色。

Because graph coloring problem belongs to NP complete problems, it can not get the optimal solution in polynomial time.

由于图的着色问题属于NP完全问题,不可能在多项式时间内得到最优解。

Matlabf programming is usually used to solve the graph coloring problem, frequent use of this principle.

详细说明:用matlabf编程求解通常的图着色问题,经常使用这个原理的。

A novel encoding scheme was presented to solve the redundancy of the integer representation for the graph coloring problem.

针对整数编码的冗余性,提出了求解图着色问题的一种新的编码方式。

So, we present group genetic algorithms to solve the graph coloring problem.

因此提出利用分组遗传算法的不定长分组编码方式来彻底解决编码冗余问题。

Then weintegrate graph coloring into fair scheduling algorithm to improve performance in polling based cooperative networks.

其中,主要利用合作式通讯的特性去同时加强长期以及短期的公平性。

Based on these,a novel hybrid genetic algorithm is proposed for the graph coloring problem.The simulation results on ten standard benchmark problems show that the proposed algorithm can obtain good quality solution and it is a potential algorithm for the problem studied.

在此基础上,提出了图着色问题的一种新的混合遗传算法,对10个标准算例的仿真结果表明,新混合遗传算法可以获得问题高质量的解,是一种有潜力的算法。

In this section, we present a method that breaks the symmetry of the graph coloring problem by matrix transformation and provide a improved general standard of genetic algorithms which can be used to solve many problems with a highly symmetric solution space .

本文提出利用矩阵变换的方法解决冗余数据带来的交叉算子失效问题,并给出一种改进的通用遗传算法标准用于解决一类具有编码冗余问题的例子。

This behavior is simple and efficient, but may destory the solving trajectory. In order to improve the performance of the system, we introduce the notion of agent compromise in distributed graph coloring problem solving with ERA.

为提高系统的求解性能,我们在求解分布式着色问题时,引入了agent妥协,通过妥协agent的行为,给难以确定赋值的主要变量赋值,从而实现求解性能的提高。

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