共查询到20条相似文献,搜索用时 781 毫秒
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为了避免遗传算法在自动组卷中存在的未成熟收敛和收敛速度慢等弱点,根据群体适应值的分布特点,采用了基于小生境的改进自适应遗传算法。该算法采用模拟小生境法选择算子进行种群选取,并对交叉算子和变异算子进行了优化,实现了交叉和变异概率的非线性自适应调整。改进后的算法明显提高了组卷的成功率和收敛速度,取得了满意的组卷效果。 相似文献
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基于改进的选择算子和交叉算子的遗传算法 总被引:9,自引:3,他引:6
为了有效解决遗传算法中收敛速度与局部最优解的矛盾,文中提出了一种具有改进的选择算子和改进的交叉算子的遗传算法。使用文中改进的选择算子,能够增加算法收敛于全局最优解的概率,从而不容易陷入局部最优,也就增加了找到最优解的概率,使用文中改进的交叉算子可以加快算法的收敛速度,从而缩短寻找最优解的时间。实验证明,这两种改进算子的结合能以较快速度收敛于全局最优解,因此能很好地解决遗传算法中收敛速度与局部最优解之间的矛盾。 相似文献
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为了有效解决遗传算法中收敛速度与局部最优解的矛盾,文中提出了一种具有改进的选择算子和改进的交叉算子的遗传算法。使用文中改进的选择算子,能够增加算法收敛于全局最优解的概率,从而不容易陷入局部最优,也就增加了找到最优解的概率,使用文中改进的交叉算子可以加快算法的收敛速度,从而缩短寻找最优解的时间。实验证明,这两种改进算子的结合能以较快速度收敛于全局最优解,因此能很好地解决遗传算法中收敛速度与局部最优解之间的矛盾。 相似文献
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目标分配的遗传算法改进研究 总被引:1,自引:0,他引:1
介绍一种目标分配的遗传算法求解方案,在此算法的基础上进行了算法改进。新算法对遗传算法涉及的初始种群、选择算子、交叉算子等进行了优化并结合微粒群算法的思想对遗传算法进行了改进。最后,通过仿真结果验证了改进算法的可行性。 相似文献
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一种新的基于遗传操作的改进型遗传算法 总被引:2,自引:0,他引:2
交叉与变异是遗传算法的重要操作,提出了一种新的基于遗传操作的改进型遗传算法.采用最优保留和改进的轮盘赌选择方法,通过基因交叉概率控制交叉,根据高斯分布改进了交叉算子和变异算子,保证了算法的全局搜索能力、局部搜索能力及收敛速度.通过标准函数的数值实验,验证了新算法的有效性. 相似文献
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针对遗传算法所存在的早熟和收敛速度慢等问题,基于低等生物的分裂生殖现象,提出了分裂算子的概念,并将该算子引入到传统遗传算法和自适应遗传算法中,对这两种遗传算法进行了改进。通过一系列多峰函数测试实验,将改进算法分别与基本遗传算法和自适应遗传算法进行比较,证明引入分裂算子后的遗传算法和自适应遗传算法不仅有效地收敛到全局最优解,而且提高了收敛速度。 相似文献
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一种基于改进遗传算法的TSP问题求解方法 总被引:2,自引:1,他引:1
通过改进经典遗传算法的交叉算子和变异算子,提出了一种改进遗传算法。介绍了该算法的基本步骤及特点,并对TSP问题进行了仿真实验。实验结果表明改进算法有效地提高了算法的收敛速度与寻优质量,在解决TSP问题时表现出良好特性,与经典遗传算法相比具有明显优势。 相似文献
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相比传统的调节方法,遗传算法具有更好的鲁棒性、最优性,能较好的实现参数的自动化调节。对标准遗传算法(SGA)进行了分析、研究,并在SGA的基础上进行了改进。改进的遗传算法从提高全局搜索性能和加快收敛速度出发,提出了改进的选择算子、交叉算子和变异算子,仿真结果表明,改进的遗传算法的全局搜索性能和收敛速度远远优于标准遗传算法。 相似文献
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用GA优化模糊控制器及其应用 总被引:1,自引:1,他引:1
用遗传算法优化模糊控制器的隶属度函数,得到优化的模糊控制器。在遗传算法设计方面,采用十进制基因编码,减少了译码的麻烦,避免了常规情况下以二进制形式编码的染色体物理意义不明显和在交叉、变异操作中容易破坏本身特性的缺点。给出了一种比较实用的交叉、变异和选择的方法,不仅使得种群进化的操作计算变得简单,而且既保留了染色体中好的特性又优化了不良的染色体。全部程序由Matlab编程实现。针对某货船,将优化后的模糊控制器在Matlab的Simulink中进行了多种情况下的仿真研究。此外,对于种群规模、进化代数对模糊控制器性能的影响也做了对比的仿真研究。由仿真结果可知,遗传算法能够有效地提高模糊控制器的性能。 相似文献
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Most search techniques within ILP require the evaluation of a large number of inconsistent clauses. However, acceptable clauses
typically need to be consistent, and are only found at the “fringe” of the search space. A search approach is presented, based
on a novel algorithm called QG (Quick Generalization). QG carries out a random-restart stochastic bottom-up search which efficiently
generates a consistent clause on the fringe of the refinement graph search without needing to explore the graph in detail.
We use a Genetic Algorithm (GA) to evolve and re-combine clauses generated by QG. In this QG/GA setting, QG is used to seed
a population of clauses processed by the GA. Experiments with QG/GA indicate that this approach can be more efficient than
standard refinement-graph searches, while generating similar or better solutions.
Editors: Ramon Otero, Simon Colton. 相似文献
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Hiroaki Imade Ryohei Morishita Isao Ono Norihiko Ono Masahiro Okamoto 《New Generation Computing》2004,22(2):177-186
In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running
on the Grid, named “Grid-Oriented Genetic algorithms (GOGAs)”, and actually “Gridify” a GA for estimating genetic networks,
which is being developed by our group, in order to examine the usability of the proposed GOGA framework. We also evaluate
the scalability of the “Gridified” GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed
in our laboratory.
Hiroaki Imade: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2001.
He received the M.S. degree in information systems from the Graduate School of Engineering, The University of Tokushima in
2003. He is now in Doctoral Course of Graduate School of Engineering, The University of Tokushima. His research interests
include evolutionary computation. He currently researches a framework to easily develop the GOGA models which efficiently
work on the grid.
Ryohei Morishita: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2002.
He is now in Master Course of Graduate School of Engineering, The University of Tokushima, Tokushima. His research interest
is evolutionary computation. He currently researches GA for estimating genetic networks.
Isao Ono, Ph.D.: He received his B.S. degree from the Department of Control Engineering, Tokyo Institute of Technology, Tokyo, Japan, in
1994. He received Ph.D. of Engineering at Tokyo Institute of Technology, Yokohama, in 1997. He worked as a Research Fellow
from 1997 to 1998 at Tokyo Institute of Technology, and at University of Tokushima, Tokushima, Japan, in 1998. He worked as
a Lecturer from 1998 to 2001 at University of Tokushima. He is now Associate Professor at University of Tokushima. His research
interests include evolutionary computation, scheduling, function optimization, optical design and bioinformatics. He is a
member of JSAI, SCI, IPSJ and OSJ.
Norihiko Ono, Ph.D.: He received his B.S. M.S. and Ph.D. of Engineering in 1979, 1981 and 1986, respectively, from Tokyo Institute of Technology.
From 1986 to 1989, he was Research Associate at Faculty of Engineering, Hiroshima University. From 1989 to 1997, he was an
associate professor at Faculty of Engineering, University of Tokushima. He was promoted to Professor in the Department of
Information Science and Intelligent Systems in 1997. His current research interests include learning in multi-agent systems,
autonomous agents, reinforcement learning and evolutionary algorithms.
Masahiro Okamoto, Ph.D.: He is currently Professor of Graduate School of Systems Life Sciences, Kyushu University, Japan. He received his Ph.D. degree
in Biochemistry from Kyushu University in 1981. His major research field is nonlinear numerical optimization and systems biology.
His current research interests cover system identification of nonlinear complex systems by using evolutional computer algorithm
of optimization, development of integrated simulator for analyzing nonlinear dynamics and design of fault-tolerant routing
network by mimicking metabolic control system. He has more than 90 peer reviewed publications. 相似文献
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一种改进的自适应遗传算法 总被引:33,自引:3,他引:30
遗传算法作为一种模仿生物自然进化过程的随机优化算法,对求解一般的全局最优问题具有较好的鲁棒性,而对于解决较复杂的优化问题则存在早熟及稳定性差的缺点。传统的自适应遗传算法虽能有效提高算法的收敛速度,却难以增强算法的鲁棒性。该文提出了一种改进的自适应遗传算法,对交叉率和变异率进行了优化,实现了交叉率和变异率的非线性自适应调整。实验结果表明,相比传统的自适应遗传算法,新算法具有更快的收敛速度和更可靠的稳定性。 相似文献
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排课问题是一个多约束、多目标的组合优化问题,并且已经被证明是一个NP完全问题。本文基于本校教学管理过程的实际情况,利用遗传算法对排课问题建立数学模型,设计了适应度函数,通过选择、交叉和变异等过程,进化得到最优解。实验结果表明该算法能够有效的解决本校的教务智能排课问题。 相似文献
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Coordinating inventory and transportation policies can lead to substantial cost savings and improved service levels especially when the companies relay on third-party logistics providers to transport the products across the supply chain. In this paper, therefore focus has been given on a supply chain system of multi-supplier, single warehouse and multi-retailer with backlogging and transportation capacity. The paper aims to suggest replenishment policies that can minimize system-wide cost by taking advantage of quantity discounts in the transportation cost structures. The problem considered in this paper has been formulated as an integer programming model. The supply chain problem is usually complex and involves massive calculations hence it is difficult to obtain an optimal solution. Therefore, to overcome this issue a Genetic Algorithm (GA) based approach has been suggested to resolve the problem. The computational results demonstrate the robustness and efficacy of the GA in optimizing replenishment policies. 相似文献
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何爱香 《计算机工程与应用》2007,43(18):242-245
提出了一种基于两轮遗传算法的用于结肠癌微阵列数据基因选择与样本分类的新方法。该方法先根据基因的Bhattacharyya距离指标过滤大部分与分类不相关的基因,而后使用结合了遗传算法和CFS(Correlation-based Feature Selection)的GA/CFS方法选择优秀基因子集,并存档记录这些子集。根据存档子集中基因被选择的频率选择进一步搜索的候选子集,最后以结合了遗传算法和SVM的GA/SVM从候选基因子集中选择分类特征子集。把这种GA/CFS-GA/SVM方法应用到结肠癌微阵列数据,实验结果及与文献的比较表明了该方法效果良好。 相似文献