自适应变异遗传算法及其应用

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.     

自适应遗传算法交叉变异算子的改进

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。     

自适应多位变异遗传算法的实现

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.     

自适应变异的遗传算法求解Flow Shop问题

针对遗传算法在求解流水车间调度问题中易出现早熟收敛的局限性．提出了自适应变异的遗传算法。该算法在运行的过程中，首先定义了一种新的衡量种群多样性的熵．然后根据熵值来确定变异概率。这种变异增强了遗传算法跳出局部最优解的能力。仿真实例的结果表明了该算法的有效性，最后利用该算法设计并实现了一个简单的服装加工智能调度器。     

个体自适应变异遗传算法

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。     

一种并行变异自适应遗传算法及其性能分析

针对基于实数编码的遗传算法收敛速度慢与收敛精度不高等问题,通过定义种群活力,提出了一种改进的自适应遗传算法．该算法中,种群活力的定义综合考虑了种群多样性和相邻代种群间相似度,众数代替平均数作为新的种群适应度参考量,并依以上两点对交叉和变异概率进行自适应调节,同时引入并行机理对变异操作进行了改进．通过仿真实例,验证了该算法具有较高的收敛速度和求解精度．最后,该算法还被应用于解决汽油调和优化调度问题．     

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

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.     

一种图像增强的自适应免疫遗传算法

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.     

云自适应遗传算法

传统自适应遗传算法(AGA)虽能有效提高收敛速度,却难以增强算法的鲁棒性.以当代种群平均适应度为期望Ex,根据云模型"3En"规则确定熵En,由X条件云发生器自适应调整交叉变异概率,提出云自适应遗传算法(CAGA).由于云模型云滴具有随机性和稳定倾向性特点,使交叉变异概率值既具有传统AGA的趋势性,满足快速寻优能力;又具有随机性,且当种群适应度最大时并非绝对的零概率值,有利于提高种群多样性,从而大大改善避免陷入局部最优的能力.典型函数优化实验表明,与标准遗传算法(SGA)和AGA相比,CAGA具有更好的收敛速度和鲁棒性.     

模拟退火自适应大变异遗传算法及其应用

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。     

Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes

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.     

Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

Research into adjusting the probabilities of crossover and mutation p_m in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. p_x and p_m 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 p_x and p_m , this paper presents the use of fuzzy logic to adaptively adjust the values of p_x and p_m 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 p_x and p_m. 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 p_x and p_m. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions     

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。     

Intelligent Mutation Rate Control in an Economic Application of Genetic Algorithms

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.     

基于变异的紧凑遗传算法

紧凑遗传算法(CGA)具有存储成本低的优点,但是其容易出现早熟。该文提出一种基于变异的紧凑遗传算法(MBCGA)。MBCGA在CGA的基础上,引进变异算子,完整地体现生态进化中的选择、遗传和变异,提高了局部寻优以及算法克服早熟的能力。试验结果表明,MBCGA保留存储成本低的优点,具有较快的收敛速度。变异算子的局部寻优作用明显。     

基于Nash均衡的自适应遗传算法

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。