改进的正交遗传算法及其在函数优化中的应用

提出了一种新的正交遗传算法(OBGA),算法的特点是利用正交数组产生初始种群,它比随机产生的初始种群更均匀分布在解空间中,而且在正交设计的基础上提出了一种新的杂交算子,与高斯变异算子相结合,提高了种群的多样性和算法的局部搜索能力,最后对6个多峰函数进行了测试.数值实验结果表明,新算法正确高效,稳定性好.     

目标分配的遗传算法改进研究

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。     

混沌蜜蜂双种群进化遗传算法

利用混沌运动的遍历性、随机性和规律性等特点,提出混沌蜜蜂双种群进化遗传算法。该算法在基于蜜蜂双种群进化遗传算法的基础上,利用混沌优化进行改善初始种群质量和利用混沌退化变异算子代替常规算法中的变异算子,避免搜索过程陷入局部极值。实验结果表明,该算法计算速度快、收敛性好,提高了常规遗传算法的收敛速度和优化效果。     

基于启发式遗传算法的航班勤务编排算法

分析了国内外机组排班流程与算法的特点、我国民航局及航空公司的相关规定,构建了航班勤务编排问题的数学模型,研究了基于启发式遗传算法的求解方法.以遗传算法为皋础,采用顺序编码描述该问题,设计了可修正冗余解的译码方法,以及基于航段的交叉算子与变异算子.为加快可行性解的求解速度,提出了启发式初始种群生成策略、缩小解空间的启发式算子与启发式变异策略.提高了遗传算法的性能,增强了算法的搜索能力,改善了勤务编排质量及优化效果.     

旅行商问题(TSP)的伪并行遗传算法

旅行商问题(TSP)是典型的NP完全组合优化问题．本文基于遗传算法求解TSP问题时的独特性,提出一种采用无性繁殖的改进伪并行遗传算法,避免了交叉算子对良好基因模式的破坏;初始种群通过贪婪算法得到并进行预处理,提高算法的收敛速度;伪并行遗传算法中子群体之间的信息交换采用孤岛模型．这些改进措施对降低算法的复杂程度、提高算法的收敛速度和全局搜索能力有重要意义．仿真研究结果表明,该算法的寻优效率较高,有效地克服了标准遗传算法的早熟收敛问题．     

基于改进遗传算法的机器人路径规划

在分析传统遗传算法的基础上提出一种移动机器人全局路径规划算法.采用方向的二进制串对染色体进行编码,在生成初始种群时,沿着正弦曲线轨迹生成部分染色体.另外选择和交叉操作采用了锦标赛选择算子和多点交叉算子,仿真结果显示本算法正确有效.     

基于混合遗传算法的固定货架拣选问题求解

针对自动化立体仓库固定货架系统拣选路径优化问题的特点,分析并设计了一种新型混合遗传算法。构造初始种群时加入了一种补充算法,遗传操作采用了一种受贪婪算法启发的交叉算子和倒位变异算子,显著改善了原有遗传算法的搜索能力。仿真结果表明该遗传算法在执行时间和优化效果两方面均能很好的满足作业要求。     

基于无约束优化和遗传算法的贝叶斯网络结构学习方法

基于无约束优化和遗传算法,提出一种学习贝叶斯网络结构的限制型遗传算法.首先构造一无约束优化问题,其最优解对应一个无向图.在无向图的基础上,产生遗传算法的初始种群,并使用遗传算法中的选择、交叉和变异算子学习得到最优贝叶斯网络结构.由于产生初始种群的空间是由一些最优贝叶斯网络结构的候选边构成,初始种群具有很好的性质.与直接使用遗传算法学习贝叶斯网络结构的效率相比,该方法的学习效率相对较高.     

一种改进的选播QoS路由算法

IPv6定义了一种新的通信模型--选播(Anycast),发送到一个选播地址的报文被传送到由该地址标识的"最近"接口之一.在深入分析选播通信服务模型和遗传算法后,提出了一种多约束的基于改进遗传算法的选播QoS路由算法.该算法采用深度优先搜索和轮盘赌相结合的方法保证初始种群的多样性,引入多种群策略和修正算子,同时对遗传算子进行了改进.仿真实验结果表明,改进后的算法提高了收敛速度,获得了较优的选播路径.     

具有自识别能力的遗传算法求解旅行商问题

为解决基本遗传算法求解旅行商(TSP)问题收敛速度慢、种群过早成熟和局部搜索能力差的问题,提出了一种具有自识别能力的遗传算法。算法的主要改进手段是,通过双向贪婪算法来构建初始种群,以提高寻找到最优解的速度;建立个体之间相似度的概念,用自识别交叉算子进行交叉操作,避免种群过早成熟。实验结果表明,与基本遗传算法相比,该算法很好地保持了群体的多样性,并具有较好的收敛速度。仿真结果验证了算法的良好性能。     

单亲遗传算法及其全局收敛性分析

序号编码的遗传算法(GA)不能在两条染色体的任意位置进行交叉,必须使用 PMX,CX和OX等特殊的交叉算子,而这些交叉算子实施起来都很麻烦.针对序号编码GA 的上述不足,提出一种单亲遗传算法(PGA).PGA采用序号编码,不使用交叉算子,而代之以 隐含序号编码GA交叉算子功能的基因换位等遗传算子,简化了遗传操作,并且不要求初始 群体具有多样性,也不存在"早熟收敛"问题.仿真结果验证了这种算法的有效性.     

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.     

Genetic algorithms for flowshop scheduling problems

In this paper, we apply a genetic algorithm to flowshop scheduling problems and examine two hybridizations of the genetic algorithm with other search algorithms. First we examine various genetic operators to design a genetic algorithm for the flowshop scheduling problem with an objective of minimizing the makespan. By computer simulations, we show that the two-point crossover and the shift change mutation are effective for this problem. Next we compare the genetic algorithm with other search algorithms such as local search, taboo search and simulated annealing. Computer simulations show that the genetic algorithm is a bit inferior to the others. In order to improve the performance of the genetic algorithm, we examine the hybridization of the genetic algorithms. We show two hybrid genetic algorithms: genetic local search and genetic simulated annealing. Their high performance is demonstrated by computer simulations.     

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.     

一种多模式协作遗传算法

该文主要针对遗传算法在实际操作应用中遗传算子和参数选取的复杂性,分析和探讨了一种多模式协作遗传算法。对于不同的优化问题以及遗传算法的不同运行阶段,引入局部遗传算法来实现一定领域中几种改进的遗传算子的适应性选取,使它们能够有效协作并且能够有效发挥各自特长,形成一种具有广泛适用性的算法模式。使用者可以在缺乏理论指导和经验参数情形下,不再凭借许多对比实验来选取合适的算子类型,方便和拓展了遗传算法的使用,并且通过试验证明了该方法的有效性。     

A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem

This paper considers the job-shop problem with release dates and due dates, with the objective of minimizing the total weighted tardiness. A genetic algorithm is combined with an iterated local search that uses a longest path approach on a disjunctive graph model. A design of experiments approach is employed to calibrate the parameters and operators of the algorithm. Previous studies on genetic algorithms for the job-shop problem point out that these algorithms are highly depended on the way the chromosomes are decoded. In this paper, we show that the efficiency of genetic algorithms does no longer depend on the schedule builder when an iterated local search is used. Computational experiments carried out on instances of the literature show the efficiency of the proposed algorithm.     

A genetic algorithm with genes-association recognition for flowshop scheduling problems

In this paper, we consider a flowshop scheduling problem with a special blocking RCb (Release when Completing Blocking). This flexible production system is prevalent in some industrial environments. Genetic algorithms are first proposed for solving these flowshop problems and different initial populations have been tested to find which is best adapted. Then, a method is proposed for further improving genetic algorithm found solutions, which consists in marking out recurrent genes association occurrences in an already genetic algorithm optimized population. This idea directly follows Holland’s first statement about nature observations. Here, proposed idea is that populations well adapted to a problem have an adapted genetic code with common properties. We propose to mark out these properties in available genetic code to further improve genetic algorithm efficiency. Implementation of this method is presented and obtained results on flowshop scheduling problems are discussed.     

基于模拟退火算法的旅行商问题的实现

旅行商问题TSP是一类典型的NP完全问题．围绕着这个问题有各种不同的求解方法,已有的算法例如动态规划法、分支限界法、回溯法等,这些精确式方法都是指数级的,根本无法解决目前的实际问题．贪心法是近似方法．无法达到比较满意的近似比。常用的遗传算法也是求解这类问题的常用方法之一。由于该问题的解是一种特殊的序列．所以遗传算法在求解该问题时的性能也并不理想。模拟退火算法具有描述简单、使用灵活、运用广泛、运行效率高和较少受到初始条件约束等优点．是解决旅行商问题的一种很好的算法。     

Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir

In this work we investigate how artificial neural network (ANN) evolution with genetic algorithm (GA) improves the reliability and predictability of artificial neural network. This strategy is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran utilizing available geophysical well log data. Our methodology utilizes a hybrid genetic algorithm–neural network strategy (GA–ANN). The proposed algorithm combines the local searching ability of the gradient–based back-propagation (BP) strategy with the global searching ability of genetic algorithms. Genetic algorithms are used to decide the initial weights of the gradient decent methods so that all the initial weights can be searched intelligently. The genetic operators and parameters are carefully designed and set avoiding premature convergence and permutation problems. For an evaluation purpose, the performance and generalization capabilities of GA–ANN are compared with those of models developed with the common technique of BP. The results demonstrate that carefully designed genetic algorithm-based neural network outperforms the gradient descent-based neural network.     

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.