一种基于种群熵的自适应遗传算法

遗传算法中的交叉概率和变异概率是影响算法行为和性能的关键所在,直接影响算法的收敛速度,甚至影响有限进化代内的收敛性。本文通过分析交叉概率和变异概率对算法的影响,设计了一种依据种群多样性和进化代数自适应调节的交叉概率和变异概率,改善了传统遗传算法存在＂早熟＂现象和算法后期收敛速度慢的不足。最后,给出了三个典型函数的模拟例子,通过与传统SGA和AGA的对比结果显示,本文的改进提高了算法的性能。     

一种新的模糊自适应模拟退火遗传算法

针对遗传算法收敛速度慢、容易"早熟"等缺点,结合模糊推理、模拟退火算法和自适应机制,提出一种改进的遗传算法--模糊自适应模拟退火遗传算法(FASAGA),并分析了该算法的性能和特点,实验研究表明,该算法比标准的遗传算法(SGA)具有更快的收敛速度和寻优效果.     

基于模糊规则优化的改进模糊遗传算法

该文针对遗传算法的特点,提出了一种基于模糊规则优化的改进模糊遗传算法及其算法结构,即用模糊控制的方法来调整遗传算法中的交叉概率和变异概率,同时寻找与控制对象相匹配的最佳模糊规则。在数学函数上的仿真结果表明,此种模糊遗传算法不仅加快了解的收敛速度,而且大大提高了解的质量。     

基于模糊规则优化的改进FGA算法

针对多目标遗传算法的特点，基于模糊集理论，提出一种基于模糊规则优化的改进模糊遗传算法及其算法结构，即用模糊控制的方法来调整遗传算法中的交叉概率和变异概率，同时寻找与控制对象相匹配的最佳模糊规则．在数学函数上的仿真结果表明，此种模糊遗传算法不仅加快了解的收敛速度，而且大大提高了解的质量．     

基于遗传退火算法的飞机定检原位工作流程优化

将遗传算法(GA)应用于飞机定检原位工作流程优化中。首先,建立原位工作流程优化模型;其次,提出"排序调整法"来保证个体对应解符合工序约束;最后采用精英选择算子。模拟退火算子和自适应机制对基本遗传算法(SGA)进行改进。仿真结果表明,改进遗传算法在最优解搜索能力上较SGA有明显提高,克服了其容易"早熟"的不足;优化后原位工作完成时间较优化前缩短19.78%,验证了GA在解决定检工作流程优化问题上的适用性。     

改进遗传算法PID参数整定及在漂白温度控制中的应用

遗传算法（GA）是一种基于群智能的全局随机优化算法。针对简单遗传算法（SGA）收敛速度慢、易于早熟等缺点,采用改进的自适应交叉算子和自适应变异算子。结合兼顾性能指标和响应过程平衡的适配函数,以多种改进方式相结合的遗传算法对PID参数进行寻优整定。并将该控制器应用于纸浆漂白温度控制中,仿真结果表明：改进遗传算法能够明显改善收敛速度和寻优效果,当被控对象存在较大纯滞后、时间常数特性较大时,采用本方法优化PID控制器参数可获得比较满意的控制效果。     

一种改进的遗传算法Scatter GA

介绍一种改进的扩散式遗传算法 Scatter GA(Sc GA)。在原简单遗传算法 Sim ple GA(SGA)的基础上进行局部改进 ,采用更接近问题实际的实数编码方式 ,增加了第 2个变异算子 ,取消了选择算子。使用改进的算法对 4个典型函数进行计算 ,并与 SGA,m icro GA(m GA) ,Steady State GA(SSGA)的结果进行比较 ,可以看出 ,改进算法在收敛速度和精度上均优于其它同类算法。     

模糊逻辑遗传算法的新方法

模糊逻辑是近年来提出的一种自适应调整策略,可以用来动态调整遗传算法的参数,以提高其性能.在此提出一种模糊逻辑遗传算法(FGA)的新模糊控制系统,它根据种群的进化速度和多样性的反馈信息,通过模糊逻辑控制器来对交叉率Pc和变异率Pm进行动态的自适应控制.实验结果表明,提出的FGA相对于简单遗传算法(SGA),不仅在与实际最优值差值上获得高1～3个数量级的精度,而且还提高了收敛的速度,较好地解决了SGA容易陷入早熟状态、某些函数进化速度慢等问题.     

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.     

基于改进权重自适应GA的冷连轧轧制规程多目标优化

针对聚合多目标优化方法的权重难以确定的问题, 提出了一种改进的权重自适应方法, 并以遗传算法为基础对冷连轧轧制规程进行多目标优化. 首先, 结合某冷轧厂实际的轧制规程优化过程, 选取等功率裕量、轧制能耗及带钢打滑概率作为优化目标, 建立了冷连轧轧制规程的多目标优化模型. 然后将改进的权重自适应遗传算法(GA)应用于不同规格的带钢轧制规程多目标优化中, 结果表明, 与实际应用的轧制规程相比, 该方法有效的降低了3个目标函数的值; 与权重自适应GA相比, 改进的权重自适应GA的针对性更强, 同时重要性高的目标收敛速度更快.     

一种新的调节交叉和变异概率的自适应算法

提出一种新的基于模糊控制策略的交叉和变异概率自适应调节算法.该算法以相邻两代群体之间平均适应度函数和标准差的差值作为输入,以交叉和变异概率的变化量作为输出.并提出了与输入相对应的自适应归一化算子以及新的基于启发式知识的模糊规则,用于交叉和变异概率的调节.对3种不同测试函数的数值仿真研究表明,与其他2种自适应模糊控制算法相比,该调节算法可使遗传算法具有更快的搜索速度和更高的搜索质量.     

Evolution computation based learning algorithms of polygonal fuzzy neural networks

We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic algorithm (GA). In the algorithm, we obtain an optimal learning constant η by GA and the experiment indicates the new algorithm always converges. Because the algorithm based on GA is a little slow in every iteration step, we propose to get the learning constant η by quantum genetic algorithm (QGA) in place of GA to decrease time spent in every iteration step. The PFNN tuned by the proposed learning algorithm is applied to approximation realization of fuzzy inference rules, and some experiments demonstrate the whole process. © 2011 Wiley Periodicals, Inc.     

Fuzzy multiple objective optimal system design by hybrid genetic algorithm

In this paper, we propose a method for solving fuzzy multiple objective optimal system design problems with GUB structure by hybridized genetic algorithms (HGA). This approach enables the flexible optimal system design by applying fuzzy goals and fuzzy constraints. In this genetic algorithm (GA), we propose the new chromosomes representation that represents the GUB structure simply and effectively at the same time. Also, by introducing the HGA that combine the proposed heuristic algorithm that makes use of the peculiarity of GUB structure to GA, the proposed approach is efficient than the previous method in finding solution.     

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.     

Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm     

基于蚁群算法的模糊控制规则的过滤简化

针对设计高维模糊控制器过程中会遇到的“规则爆炸”问题,利用蚁群算法进行控制规则的过滤简化。为了用尽量少的规则得到尽可能好的控制效果,利用蚁群算法在饵决组合优化问题中的强大优势,在已有的完备规则中优选出若干条规则嵌人模糊控制器。采用带有时间窗口的蚁群算法去克服遗传算法优选模糊控制规则时可能产生的规则不连续的问题。该文还从遗传算法和蚁群算法工作机制的角度分析了对这两种算法加入约束条件的可操作性。以单级倒立摆控制系统为对象进行仿真研究,最后的仿真结果表明该文方法可以使模糊控制规则具有更好的简化效果和鲁棒性,并能具有好的控制效果。     

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     

Automatic magnetic resonance image segmentation by fuzzy intercluster hostility index based genetic algorithm: An application

Segmentation of magnetic resonance (MR) images plays an important role in the medical science or clinical research. In this article, an application of a genetic algorithm (GA) based segmentation algorithm is presented for automatic grouping of unlabeled pixels of the MR images into different homogeneous clusters. Before the segmentation, the information about the optimal number of segments as well as the underlying pixel distribution of an image is not required in this method. The centroid of different segments is demarcated as active/inactive centroid by the fuzzy intercluster hostility index. After that, the test images are segmented by the selected active centroids. The optimal number of segments and their respective centroids are determined by this method. A performance comparison is manifested between the fuzzy intercluster hostility index based GA method and the well-known automatic clustering using differential evolution (ACDE) algorithm and one genetic algorithm based non-automatic algorithm with the help of two real life MR images. The comparison depicted the superiority of the GA based automatic image segmentation method with the help of fuzzy intercluster hostility index over other two algorithms.     

基于遗传算法的自适应聚类与MQAM星座识别

提出了一种基于星座聚类的MQAM调制识别新方法,运用一种改进的基于遗传算法的自适应聚类算法对MQAM星座进行重构和识别。该自适应聚类算法利用遗传算法的高效全局搜索特性,克服了模糊C-均值算法对初始聚类中心和样本输入次序敏感等不足,结合聚类有效性分析实现了聚类中心数目的自适应调整。仿真结果表明,基于该聚类算法的MQAM信号调制阶数识别方法是有效的。