国家自然科学基金(70071042)

作品数：34被引量：103H指数：6
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相关作者：康立山黄樟灿朱琪朱金寿杨勇刚更多>>
相关机构：武汉理工大学武汉大学中南民族大学更多>>
相关期刊：《武汉理工大学学报》《武汉理工大学学报（交通科学与工程版）》《Wuhan University Journal of Natural Sciences》《计算机工程与应用》更多>>
相关主题：EVOLUTIONARY_COMPUTATIONGENETIC_ALGORITHMSOLVING收敛性TSP更多>>
相关领域：理学自动化与计算机技术交通运输工程建筑科学更多>>
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基于基因库求解TSP的改进的反序—杂交算法被引量：5
《计算机工程与应用》2005年第7期37-39,共3页卿翊轩 康立山 陈毓屏 
国家自然科学基金(编号:70071042;60073043;60133030)
文章对求解TSP的“反序-杂交”算法在反序时城市位置的选择方式上作了改进,同时限制对每个个体一次循环中反序的次数,提出一种“见好就收”的策略,并利用“基因库”(即保存了好边的矩阵)的思想来指导反序-杂交。实验证明,改进的算法在...
关键词：旅行商问题 反序-杂交 见好就收 基因库 
一个多目标优化演化算法的收敛性分析框架被引量：6
《计算机应用研究》2005年第2期68-70,共3页覃俊 康立山 
国家自然科学基金资助项目(69635030,60073043,70071042)
由于演化算法求解多目标优化问题所得结果是一个优化解集———Pareto最优集,而现有的演化算法收敛性分析只适合针对单目标优化问题的单个最优解。利用有限马尔科夫链给出了演化算法求解多目标优化问题的收敛性分析框架,并给出了一个分...
关键词：多目标优化 演化算法 PARETO前沿 收敛性 有限马尔科夫链 
基于遗传算法的信息检索技术被引量：14
《计算机工程》2004年第9期74-75,108,共3页徐斌 刘赛 康立山 郑刚 
国家自然科学基金资助项目:复杂系统中的演化算法(70071042);自动程序设计理论与应用(60073043);进化计算理论;方法及应用(6013301
随着万维网（WWW）中信息量呈指数的增长，人们可以使用许多的信息收集工具来获得网络中的信息。但要使检索到的信息在满足用户个性化需求方面，即具有高准确率又有高回收率，则是一件很困难的事情。为了解决以上问题，该文阐述了遗传...
关键词：遗传算法 信息检索 用户兴趣模型 万维网 信息收集 
Solving A Kind of High Complexity Multi-Objective Problems by A Fast Algorithm
《Wuhan University Journal of Natural Sciences》2003年第S1期183-188,共6页Zeng San-you, Ding Li-xin, Kang Li-shanDepartment of Computer Science,China University of GeoSciences, Wuhan 430074, Hubei, China Department of Computer Science, Zhuzhou Institute of Technology , Zhuzhou 412008, Hunan, China State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, China 
Supported by the National Natural Science Foundation of China(60204001,70071042,60073043,60133010)and Youth Chengguang Project of Science and Technology of Wuhan City(20025001002)
A fast algorithm is proposed to solve a kind of high complexity multi-objective problems in this paper. It takes advantages of both the orthogonal design method to search evenly, and the statistical optimal method to ...
关键词：evolutionary algorithms orthogonal design multi-objective optimization Pareto-optimal set 
A New Definition and Calculation Model for Evolutionary Multi-Objective Optimization被引量：1
《Wuhan University Journal of Natural Sciences》2003年第S1期189-194,共6页Zhou Ai-min, Kang Li-shan, Chen Yu-ping, Huang Yu-zhenState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, China 
Supported by the National Natural Science Foundation of China(70071042,60073043,60133010)
We present a new definition (Evolving Solutions) for Multi-objective Optimization Problem (MOP) to answer the basic question (what's multi-objective optimal solution?) and advance an asynchronous evolutionary model (M...
关键词：evolving equilibrium evolving solutions MINT model multi-objective optimization 
Do Search and Selection Operators Play Important Roles in Multi-Objective Evolutionary Algorithms:A Case Study被引量：1
《Wuhan University Journal of Natural Sciences》2003年第S1期195-201,共7页Yan Zhen-yu, Kang Li-shan, Lin Guang-ming ,He MeiState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, ChinaSchool of Computer Science, UC, UNSW Australian Defence Force Academy, Northcott Drive, Canberra, ACT 2600 AustraliaCapital Bridge Securities Co. ,Ltd, Floor 42, Jinmao Tower, Shanghai 200030, China 
Supported by the National Natural Science Foundation of China(60133010,70071042,60073043)
Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search an...
关键词：multi-objective evolutionary algorithm convergence property analysis search operator selection operator Markov chain 
A New Evolutionary Algorithm for Solving Multi-Objective Optimization Problems被引量：1
《Wuhan University Journal of Natural Sciences》2003年第S1期202-206,共5页D Chen Wen-ping, Kang Li-shanState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, China 
Supported by the National Natural Science Foundation of China (6013301,60073043,70071042)
Multi-objective optimization is a new focus of evolutionary computation research. This paper puts forward a new algorithm, which can not only converge quickly, but also keep diversity among population efficiently, in ...
关键词：evolutionary computation multi-objective optimization Pareto-optimal set fitness-sharing 
A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking被引量：1
《Wuhan University Journal of Natural Sciences》2003年第S1期207-211,共5页Shi Chuan, Kang Li-shan, Li Yan, Yan Zhen-yuState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei,China 
Supported by the National Natural Science Foundation of China(60073043,70071042,60133010)
Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has so...
关键词：multi-objective optimal problem multi-objective optimal evolutionary algorithm Pareto dominance tree structure dynamic space-compressed mutative operator 
Evolutionary Graph Drawing Algorithms被引量：1
《Wuhan University Journal of Natural Sciences》2003年第S1期212-216,共5页Huang Jing-wei, Wei Wen-fangSchool of Computer, Wuhan University, Wuhan 430072, Hubei, ChinaComputer Center, Yunyang Medical College, Shiyan 442000, Hubei, China 
Supported by the National Natural Science Foundation of China(60133010,60073043,70071042)
In this paper, graph drawing algorithms based on genetic algorithms are designed for general undirected graphs and directed graphs. As being shown, graph drawing algorithms designed by genetic algorithms have the foll...
关键词：graph drawing ALGORITHMS genetic algorithms 
A Gene-Pool Based Genetic Algorithm for TSP被引量：6
《Wuhan University Journal of Natural Sciences》2003年第S1期217-223,共7页Yang Hui, Kang Li-shan, Chen Yu-pingState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, China 
Supported by the National Natural Science Foundation of China(70071042,60073043,and 60133010)
Based on the analysis of previous genetic algorithms (GAs) for TSP, a novel method called Ge- GA is proposed. It combines gene pool and GA so as to direct the evolution of the whole population. The core of Ge- GA is t...
关键词：Genetic Algorithm Gene Pool minimal spanning tree combinatorial optimization TSP 
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