图着色问题

基于图着色问题的特点，设计了一种新的、有效的杂交算子。

An effective crossover is designed according to the characteristic of the GCP .

DNA自组装计算模型研究及其在图着色问题中的应用

Research on Computational Model of DNA Self-Assembly and Its Application in Graph Coloring Problem

图着色问题的表面DNA算法

An Algorithm of Gragh Coloring Problem by DNA Computing

四色和K色图着色问题的瞬态混沌神经网络解法

Artificial Neural Network with Transient Chaos for Four - Coloring Map Problems and K-Colorability Problems

图着色问题（GCP）是NP完全问题。

Graph Coloring problem ( GCP ) is an NP hard problem .

它的应用已涉及许多领域，如旅行商问题、指派问题、job-shop调度问题、图着色问题等等，并且取得了很好的效果。

It has been used successfully in many fields , such as TSP , assignment problem , job-shop scheduling and graph coloring .

从蚁群算法提出至今近20年的历程里，蚁群算法成功地求解了许多NP完全的组合优化问题，例如旅行商问题、二次分配问题、Job-shop问题、车辆路径问题和图着色问题等。

In the past two decades , ACO algorithms are successful in applications of many NP-hard problems , such as traveling salesman problem , quadratic assignment problem , job-shop problem , vehicle-routing problem and graph coloring problem and so on .

采用一种基于退火策略的混沌神经网络（ACNN）算法求解四色图着色问题。

This paper adopts a kind of chaotic neural network algorithm based on annealing strategy ( ACNN ) for solving four-coloring map problem .

图着色问题的细胞神经网络算法研究

Research on algorithm of graph coloring by using cellular neural networks

图着色问题是著名的NP-完全问题。

The graph coloring problem is a well-known NP-complete problem .

四色图着色问题的混沌神经网络解法

Chaotic Neural Network Algorithm Based on Annealing Strategy for Solving Four-coloring Map Problem

图着色问题的一个最小冲突权值学习算法

A Min-conflict Weight-learning Algorithm for Graph Coloring Problem

若干图着色问题的研究

On Some Graph Colorings

仿真结果表明，这是一个能有效求解四色图着色问题的全局最优化算法。

The simulation result shows that ACNN is a global optimization algorithm which can effectively solve four-coloring map problem .

在优化的过程中采用了改进的蚁群算法，并结合了图论中的图着色问题。

The coloring problem of graph theory is combined with an improved ant colony algorithm to plan optimally the overhaul .

之后根据覆盖区选取方案，信道分配问题可以转化成平面图着色问题。

According to the program after the selected coverage area , the channel allocation problem can be transformed into a floor plan rendering .

在此基础上提出了求解图着色问题的一种新的遗传算法，并证明了其全局收敛性。

Based on these , a novel genetic algorithm for GCP is presented and its convergence to global optimal solution with probability one is proved .

图着色问题是一个经典的组合优化问题，无论在理论上还是工程应用上均有一个良好的应用背景，但它也是典型的NP-完全问题。

Graph coloring problem ( GCP ) is one of the most widely studied combinatorial optimization problems with many applications both in theory and engineering . But it is also a well-known NP-complete problem .

本文将新的遗传算法应用于图着色问题中，并在一些标准算例上进行仿真实验。实验结果表明，改进的遗传算法对于各种类型的图形都能获得良好的寻优能力。

Finally , The simulation experiments on some standard coloring instances are made and the simulation results indicate that the new improved genetic algorithm has good ability of searching optimal solution for various types of graphs .

基于粘贴模型的图顶点着色问题的DNA算法

DNA algorithm of graph vertex coloring problem based on sticker model

本文给出了图顶点着色问题的DNA粘贴算法。

In the dissertation , DNA sticker algorithm of vertex-coloring problems is given out .

基于kn连接关系的图的着色问题与折叠法

" Folding Method " and Coloring of the Graphs Based on k_n Connecting Relations

本文着重介绍了用蚂蚁算法解k色图的着色问题的详细步骤。

The k - Graph Coloring procedure was introduced .

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。

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

本学位论文主要考虑图的着色问题。

This dissertation is devoted to study the graph coloring problems .

图的着色问题的一个近似算法

An approximate algorithm for graph colouring problems

图的着色问题是一个被广泛研究的组合优化问题。

The graph coloring problem ( GCP ) is a widely studied combinatorial optimization problem .

图的着色问题一直是图论研究中的重要问题之一，有着重要的理论意义和实用价值。

The coloring of the graphs has been one of the main problems . It has important theoretical and practical value .

图的着色问题是由地图的着色提出的，它是最著名的NP-完全问题之一。

The graph coloring problem is extended from the map coloring , it is one of the most famous NP ─ complete problem .

事实上，许多现实生活中的问题例如考试时间表问题和任务分配问题等都可以被模拟成图的着色问题的拓展。

In fact , several classes of reallife problems such as examination timetabling and frequency assignment can be modeled as Graph Coloring Problem extensions .