共查询到16条相似文献,搜索用时 62 毫秒
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为了改善变异操作在遗传算法中的作用,提出自适应变异遗传算法,其变异操作能根据种群进化代数和个体的适应度值自适应地确定每个个体的变异概率,从而在保留遗传算法当前最优解的同时,维持了群体的多样性,提高了算法的全局搜索能力.与传统遗传算法相比,自适应变异遗传算法的离线性能和在线性能都有较大的改善.本文在实际应用中,将自适应变异遗传算法应用于估计动力学参数取得了较好的结果. 相似文献
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自适应遗传算法交叉变异算子的改进 总被引:23,自引:7,他引:23
标准遗传算法采用固定的交叉率和变异率,对于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,却难以提高优良解的多样性,算法的鲁棒性仍有待改善。文章提出了一种改进的自适应遗传算法,对交叉算子和变异算子进行了优化,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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自适应多位变异遗传算法的实现 总被引:1,自引:0,他引:1
Genetic algorithm is a widely used optimization method. Crossover and mutation are two Basicl operatorsof the genetic algorithm. On the basis of analyzing the principles of simple genetic algorithm and discussing its exist-ing problems of crossover point and mutation bit, this paper presents a way of the adaptive multiple bit mutation ge-netic algorithm , which not only can keep the population diversity but also has quicker convergence speed. The resultsof the multi-modal function optimization show that the adaptive multiple bit mutation genetic algorithm is practical and efficient. 相似文献
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个体自适应变异遗传算法 总被引:1,自引:0,他引:1
该文从染色体个体个性化和染色体编码基因位个性化两个方面对标准变异算子进行改进,提出了个体自适应变异遗传算法。通过给不同个体不同基因位分别赋予不同的变异概率,提高变异操作的效率,加快收敛速度。实验表明,个体自适应变异遗传算法的性能明显优于标准遗传算法。 相似文献
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遗传算法中交叉和变异概率选择的自适应方法及作用机理 总被引:40,自引:3,他引:37
在指出了传统遗传算法中交叉和变异概率的选择具有盲目性的基础上, 提出了遗传算法中交叉和变异概率选择的改进措施, 对其作用机理进行了深入的分析, 指出改进算法体现了自适应策略. 用一个非常复杂的数学函数对新算法进行了测试, 结果表明改进算法克服了传统遗传算法难以解决的早熟和局部收敛的问题. 相似文献
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一种图像增强的自适应免疫遗传算法 总被引:1,自引:0,他引:1
图像增强处理中,Tubbs曾将几种常用的非线性变换函数表示成一个归一化的非完全Beta函数,进行图像增强方面的研究,但确定Beta函数的参数仍是一个复杂的问题.现将自适应免疫遗传算法应用到图像的增强处理中,利用自适应免疫遗传算法的快速搜索能力,对给定的测试图像,自适应地变异、搜索、直至最终确定变换函数的最佳参数α,β值,从而实现图像的自适应增强.与穷举法相比,大大节约了求解的时间和计算的复杂度,提供了一个解决图像增强方面问题的途径.现通过对自然图像的仿真实验可以看出上述方法的有效性. 相似文献
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模拟退火自适应大变异遗传算法及其应用 总被引:4,自引:0,他引:4
为了克服遗传算法易陷入局部最优或早熟问题,提出了一种模拟退火大变异遗传算法,采用了大比例优秀个体保护策略,以保证算法的收敛性。应用该算法求解旅行商问题的仿真实验证明了它能较快地收敛到最优解或准最优解。 相似文献
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Genetic algorithms are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. Under their initial formulation, the search space solutions are coded using the binary alphabet, however other coding types have been taken into account for the representation issue, such as real coding. The real-coding approach seems particularly natural when tackling optimization problems of parameters with variables in continuous domains.A problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of population diversity. The mutation operator is the one responsible for the generation of diversity and therefore may be considered to be an important element in solving this problem. For the case of working under real coding, a solution involves the control, throughout the run, of the strength in which real genes are mutated, i.e., the step size.This paper presents TRAMSS, a Two-loop Real-coded genetic algorithm with Adaptive control of Mutation Step Sizes. It adjusts the step size of a mutation operator applied during the inner loop, for producing efficient local tuning. It also controls the step size of a mutation operator used by a restart operator performed in the outer loop, for reinitializing the population in order to ensure that different promising search zones are focused by the inner loop throughout the run. Experimental results show that the proposal consistently outperforms other mechanisms presented for controlling mutation step sizes, offering two main advantages simultaneously, better reliability and accuracy. 相似文献
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Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms 总被引:4,自引:0,他引:4
Jun Zhang Henry Shu-Hung Chung Wai-Lun Lo 《Evolutionary Computation, IEEE Transactions on》2007,11(3):326-335
Research into adjusting the probabilities of crossover and mutation pm in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. px and pm greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of px and pm , this paper presents the use of fuzzy logic to adaptively adjust the values of px and pm in GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of px and pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA using fixed values of px and pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions 相似文献
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基于改进遗传算法的多脉冲交会轨道优化研究 总被引:1,自引:0,他引:1
最优空间多脉冲轨道交会是一个复杂的非线性系统。由于结构复杂,存在非线性特性,影响系统的快速性和实时性。建立最优空间多脉冲轨道交会模型,传统遗传算法不能满足要求,因此提出了在自适应遗传算法的基础上引入多位变异的多变异位自适应遗传算法,对空间多脉冲交会轨道优化进行了求解。多变异位自适应遗传算法增加了种群的多样性,可避免算法的早熟收敛现象。仿真结果表明,利用多位变异自适应遗传算法求解空间多脉冲交会轨道优化效果好,避免了早期收敛,提高了全局寻优能力,为多脉冲交会轨道优化提供了较好的方法。 相似文献
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Michael Kurtis Maschek 《Computational Economics》2010,35(1):25-49
This work represents the first application of two-level learning in genetic algorithms in an economic environment in which
the fitness value of potential rules are complementary across individuals. Two-level learning, or self-adaptation, incorporates
certain strategy parameters into the representation of each individual. In this work, these strategy parameters provide the
likelihood of mutation for the individual. These strategy parameters evolve by means of mutation and recombination, just as
the object variables do. It is argued that self-adaptation over the parameter governing mutation can replace the election
operator proposed by Arifovic (1994) in order to attain convergence to the rational expectations equilibrium. While both adaptive
mutation and the election operator are sufficient for convergence, self-adaptation may be more appropriate when being compared
with real-world or experimental economic data. Through analysis of a static environment it is shown that this convergence,
however, will require a strong selective pressure only attained through a transformation of the baseline fitness function. 相似文献
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基于Nash均衡的自适应遗传算法 总被引:5,自引:0,他引:5
李莉 《计算机工程与应用》2004,40(33):86-88
文章针对遗传算法中存在算法“搜索能力”和“收敛能力”的矛盾问题,提出了在遗传算法中引入博弈理论,将“搜索能力”和“收敛能力”看成博弈中的两个参与者,利用Nash均衡理论协调处理这一对矛盾,达到“双赢”目的,即在保证全局最优的同时提高收敛速度。通过理论分析和实例表明该算法的优越性。 相似文献