基于改进遗传算法的多脉冲交会轨道优化研究

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。     

Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process

Traditional genetic algorithms use only one crossover and one mutation operator to generate the next generation. The chosen crossover and mutation operators are critical to the success of genetic algorithms. Different crossover or mutation operators, however, are suitable for different problems, even for different stages of the genetic process in a problem. Determining which crossover and mutation operators should be used is quite difficult and is usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and mutation operators to generate the next generation. The crossover and mutation ratios change along with the evaluation results of the respective offspring in the next generation. By this way, we expect that the really good operators will have an increasing effect in the genetic process. Experiments are also made, with results showing the proposed algorithm performs better than the algorithms with a single crossover and a single mutation operator.     

遗传算法用于结晶过程动力学参数辩识

遗传算法是一类随机优化方法。常被用于解决复杂的优化问题,基于群体的搜索,重组和变异是遗传算法区别于其他优化方法的主要特征。文章中将遗传算法应用于过饱和溶液Ｌｉ２Ｏ．３Ｂ２Ｏ３－Ｈ２Ｏ体系结晶过程动力学参数辨识,确定了结晶反应速率常数、热力学平衡浓度和表观反应级数。     

图文法遗传算法

本文讨论了进化神经网络的编码表示机制，分析了它们的优缺点；提出了遗传算法的一种图文法编码表示机制，给出了相应的算子定义，以及模式、模式长度及其阶的定义；证明了一个基于图文法表示机制的遗传算法模式定理，描述了交叉和突变对模式作用的效果。     

应用遗传算法解决装配线平衡问题

文章针对装配线平衡问题,提出了一种周期性自适应交换、变异遗传算法,通过实验求解表明,该算法是解决装配线问题的有效算法,很好地解决了简单遗传算法容易早熟收敛的问题,大大改善了简单遗传算法的性能。     

A study on the convergence of genetic algorithms

This paper extends genetic algorithms to achieve fast solutions to difficult problem. To accomplish this, we present empirical results on the terminated condition by bias and the functionized model of mutation rate in genetic algorithms. The terminated condition by bias enable to reducing computation time(CPU time) according to limitted and pre-estimated number of generations. The functionized model of mutation operator reducing computation time and improving solution should be accomplished by applying quite low mutation rate on the continuing generation with remaining 95 percentage of bias.     

自适应伪并行遗传算法及其性能分析

提出了一种度量种拜多样性的新指标，将其应用于交又概率和变异概率两个参数的自适应调整，再将自适应的思想和并行计算的思想结合起来，提出了一种在个人计算机上实现的改进自适应遗传算法一自适应伪并行遗传算法(APPGA)。对几种典型的多峰值函数求极值，结果表明：该算法的全局搜索能力和收敛速度都远优于标准遗传算法，是一种比较实用的算法。     

基于遗传模拟退火的QoS单播路由算法

针对QoS的问题,设计了一种融合遗传算法和模拟退火算法的QoS路由算法,在遗传算法选择算子上借鉴了小生境的思想,避免了遗传初期有效基因的丢失;在遗传算法交叉算子和变异算子方面使用了自适应算子,使之能更好地适应网络的变化。通过与传统遗传算法进行比较,进一步说明了本算法的有效性。     

离散时间线性系统最优控制的遗传算法实现

本文提出了求解控制变量受区间约束情形的离散时间线性系统最优控制的遗传算法,在遗传算法框架下给出了离散时间线性系统最优控制问题可行解的编码及初始化方法,设计了选择、交叉、变异等遗传算子,并对初始化方法及各种遗传算子的可行性给出理论分析。     

Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis

Genetic algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the naturalevolution principles of populations. These algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation.Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.     

基于遗传算法求解TSP问题的一种新方法

针对基于遗传算法求解TSP的效率问题,提出了一种基于位操作编码技术,并给出了基于位操作的交配、变异等基本操作的实现方法,有效地提高了计算过程中的空间利用率和计算效率。     

基于交叉与变异组合的TSP问题研究

遗传算法是一种在自然选择与遗传机制基础上的随机化的搜索类算法,是求解TSP(Travelling Salesman Problem)问题的一种常用算法。但是该算法在解决TSP问题时,存在着收敛速度过慢,容易出现早熟的问题。本文针对该问题,创新性地提出使用5种交叉算法和3种变异算法进行组合的算法设计,得出15种不同的组合方法,然后使用Java语言进行编程实验,最后通过对中国144个城市相对坐标（CHN144）的实例进行测试,证明了在使用交叉算法与变异算法进行组合得出的15种组合方法中,使用三交换交叉算法与逆序变异算法进行结合,这种组合方式的遗传算法在解决TSP这一问题时能够取得最优的效果。     

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.     

Some results about the Markov chains associated to GPs and general EAs

Geiringer's theorem is a statement which tells us something about the limiting frequency of occurrence of a certain individual when a classical genetic algorithm is executed in the absence of selection and mutation. Recently Poli, Stephens, Wright and Rowe extended the original theorem of Geiringer to include the case of variable-length genetic algorithms and linear genetic programming. In the current paper a rather powerful finite population version of Geiringer's theorem which has been established recently by Mitavskiy is used to derive a schema-based version of the theorem for nonlinear genetic programming with homologous crossover. The theorem also applies in the presence of “node mutation”. The corresponding formula in case when “node mutation” is present has been established.The limitation of the finite population Geiringer result is that it applies only in the absence of selection. In the current paper we also observe some general inequalities concerning the stationary distribution of the Markov chain associated to an evolutionary algorithm in which selection is the last (output) stage of a cycle. Moreover we prove an “anti-communism” theorem which applies to a wide class of EAs and says that for small enough mutation rate, the stationary distribution of the Markov chain modelling the EA cannot be uniform.     

基于选择性集成的整数编码遗传算法及TSP问题求解

基于遗传算法的求解TSP(Traveling Salesman Problem)研究是近几年的研究热点.设计高效的遗传算法求解,有重要的理论意义和实用价值.本文考察了基于整数编码的遗传算法的选择算子、交叉算子、变异算子,运用选择性集成的思想,将几种算子集成,随进化的进程对交叉概率和变异概率做自适应调整,用Matlab编写遗传算法程序,求解中国31城市TSP问题,获得了优于目前同类工作的结果.     

用改进的遗传算法设计交换式工业以太网拓扑

本文提出了使用改进的遗传算法来优化和设计工业控制网络的具体方法。针对交换式工业以太网的冗余拓扑结构和数据通信特点,将网络拓扑设计问题等价为一个多目标优化问题,即一方面要减少子网间通信量,同时需要平衡各个子网的通信负荷;此外,交换机物理条件的限制也为该优化问题设置了约束条件。在设计具体的遗传算法时,从编码
码方案、交叉概率和变异概率等三个方面进行改进,克服了基本遗传算法全局搜索能力较差、容易导致局部最优,以及收敛速度和全局收敛性存在矛盾等缺点。最后,仿真结果验证了该改进算法的有效性。     

一种基于决策图贝叶斯网络的强度Pareto进化算法

提出了一种基于决策图贝叶斯网络的强度Pareto进化算法,该算法把贝叶斯概率模型结合到多目标进化算法中,通过构造和学习网络来替代传统进化算法中的交叉重组和变异等遗传操作,避免对大量参数的人工设置和重要构造块的破坏．求解多目标背包问题的仿真结果表明,所提算法可以快速收敛到较好的Pareto前沿,有很强的鲁棒性．     

用遗传算法画无向图

本文提出了一个新的画一般无向图的遗传算法.以前的无向图画图算法将顶点数较多且无弦的圈画成了凹多边形,为了克服这一缺点,本文的遗传算法设计了全新的变异算子--单点邻域变异,并在适应度函数中增加用于产生对称画法的分量,可将这种图画成凸多边形.新算法的优点是方法简单,易于实现,画出的图形美观,其灵活之处在于准则的权重可以改变.实验结果表明,在相同条件下,本文算法画出的图形要比标准遗传算法画出的图形美观.     

Selection of Heterogeneous Fuzzy Model Ensembles Using Self-adaptive Genetic Algorithms

The problem of model selection to compose a heterogeneous bagging ensemble was addressed in the paper. To solve the problem, three self-adapting genetic algorithms were proposed with different control parameters of mutation, crossover, and selection adjusted during the execution. The algorithms were applied to create heterogeneous ensembles comprising regression fuzzy models to aid in real estate appraisals. The results of experiments revealed that the self-adaptive algorithms converged faster than the classic genetic algorithms. The heterogeneous ensembles created by self-adapting methods showed a very good predictive accuracy when compared with the homogeneous ensembles obtained in earlier research.     

选择和变异操作下遗传算法的收敛性研究

文中分析了选择和变异操作下遗传算法的收敛性问题，以较简洁的方式给出了连续空间中群体概率密度演变公式的证明，给出了离散空间中群体的演变过程收敛到全局最优状态的一个充分条件。