基于PPGA-MBP的神经网络优化及其应用

多物种并行进化遗传算法应用于神经网络拓扑结构的设计,开辟了新的研究领域,论文提出伪并行(PPGA-MBP)混合遗传算法,结合改进的BP算法优化多层前馈神经网络的拓扑结构。算法采用实数编码来克服传统二进制编码的精度不足问题,并设计基于层次的杂交算子允许结构相异的个体杂交重组成新的个体,适应度函数更是综合考虑了均方误差、网络结构复杂度和网络的泛化能力等因素。实验证明取得了明显的优化效果,提高了神经网络的自适应能力和泛化能力,具有全局快速收敛的性能。论文还运用该算法建立了工业增产值经济预测网络模型,将网络预测值和多项式拟合值进行了对比分析。     

基于改进遗传BP网络的飞机故障诊断研究

故障诊断对于事故后快速恢复具有重要意义.针对飞机系统的故障诊断研究,本文提出一种基于改进遗传算法优化的BP神经网络智能诊断技术.采用基于实数编码的遗传算法优化神经网路的权值和阀值,克服了BP神经网络收敛速度慢、易陷入局部极小的缺点,并通过Matlab仿真.用实例验证了经过优化的BP神经网络的训练步数得到大大减少,准确性有所提高,泛化能力也得到提升,增强了网络的学习能力,具有一定的实用性.     

基于伪并行混合遗传算法的神经网络优化

在分析并行多物种遗传算法应用于神经网络拓扑结构的设计和学习之后，提出一种伪并行遗传（PPGA-MBP）混合算法，结合改进的BP算法对多层前馈神经网络的拓扑结构进行优化。算法编码采用基于实数的层次混合方式，允许两个不同结构的网络个体交叉生成有效子个体。利用该算法对N-Parity问题进行了实验仿真，并对算法中评价函数各部分系数和种群规模对算法的影响进行了分析。实验证明取得了明显的优化效果，提高了神经网络的自适应能力和泛化能力，具有全局快速收敛的性能。     

遗传神经网络在模拟电路故障诊断中的应用

故障诊断对于事故后快速恢复具有重要的意义.模拟电路故障诊断有许多方法,提出了一种基于遗传算法优化的BP神经网络智能诊断技术.该方法采用基于实数编码的遗传算法优化神经网络权值和阈值,代替了原来BP网络随机设定的初始权值和阈值.然后再用改进的BP算法用已由遗传算法确定的空间对网络进行精确搜索.实验仿真结果表明基于遗传算法优化过的神经网络的训练步数得到大大的减少,泛化能力也得到提高.克服了传统BP算法的收敛速度慢,容易陷入局部极小的缺点.     

基于神经网络集成的软件可靠性预测研究

为解决软件可靠性预测精度差和泛化能力不强问题,提出一种遗传算法集成神经网络的软件可靠性预测模型.通过遗传算法对神经网络集成权重进行了优化,并用主成分分析方法对软件属性度量数据进行了预处理,降低数据维数,简化神经网络的结构,加快神经网络的运算速度.仿真实验结果表明,基于遗传算法集成神经网络的软件可靠性预测模型同BP网络、LVQ网络和PNN网络相比具有更好的预测精度和泛化能力.     

遗传算法三种编码策略的比较研究

分析了遗传算法的二进制、实数、十进制编码策略实现方法,根据各编码的特点,设计了相应的改进遗传策略。以前馈神经网络权值优化问题为例,用计算机仿真实验的方法研究了三种编码策略对各遗传算法性能的影响。研究结果表明,若同时强调搜索效率和搜索准确性,宜采用实数编码的改进遗传算法;若只强调搜索准确性,则应优先考虑十进制编码的改进遗传算法。研究的结论为遗传算法在高维连续参数优化问题中编码策略的选取提供了理论指导。     

遗传前馈神经网络在函数逼近中的应用

人工神经网络具有高计算能力、泛化能力和非线性映射等特点,被成功应用于众多领域,但缺乏用于确定其网络拓扑结构、激活函数和训练方法的规则。该文提出利用遗传算法优化前馈神经网络的方法,将网络结构、激活函数和训练方法等编码作为个体,发现最优或次优解,针对特定问题设计较理想的前馈神经网络。介绍遗传算法的具体步骤,对非线性函数逼近进行实验,结果表明优化后前馈神经网络的性能优于由经验确定的前馈神经网络,验证了本文方法的有效性。     

基于遗传算法的径向基神经网络及其在同时测定Fe Mn Cu Zn中的应用(英文)

介绍了两种新的基于遗传算法的径向基神经网络(GA-Based RBFNN)训练算法。这两种算法均将遗传算法用于优化径向基神经网络的聚类中心和网络结构。第一种GA-Based RBFNN算法对所有训练样本采取二进制编码构成个体,优化径向基函数中心的选取和网络结构;第二种GA-Based RBFNN算法中,RBFNN采用自增长算法训练网络隐含层中心、采用十进制对距离因子ε编码构成染色体,优化网络。将两种GA-Based RBFNN算法应用于Fe、Mn、Cu、Zn同时测定的光谱解析,计算结果表明,本文的GA-Based RBFNN算法较通常的遗传算法与径向基人工神经网络(GA-RBFNN)联用,即在GA选择变量的基础上,再用RBFNN作数据解析的GA-RBFNN方法,在增强网络的泛化能力、提高预测的准确性等方面具有明显的优势。从这两种GA-Based RBFNN的比较看,第二种算法在性能上优于第一种算法。     

基于改进遗传算法的深度神经网络优化研究

深度前馈神经网络在分类和回归问题上得到了很好的应用,但网络性能极大程度上受到其结构和超参数影响.为了获得高性能的神经网络,首先对遗传算法的选择策略进行改进,之后利用该改进遗传算法,采用二进制编码与实数编码的混合编码策略对深度前馈神经网络层数、每层节点量以及学习率和权重进行优化.改进的选择策略,在最优保存策略的基础上从父代和子代合并的2n个个体中,以一定的概率选择部分适应值较差个体作为新父代,以增加种群多样性,避免陷入局部最优.同时引入dropout方法减少网络过拟合训练数据.使用Ring、Breast cancer、Twonorm、Heart、Blood、Ionosphere、Monk共7个数据集进行数值实验,并与其他相关文献中的算法比较,仿真结果表明,改进的遗传算法能搜索到较高性能的神经网络.     

基于实数编码遗传算法的混合神经网络算法

该文比较了神经网络与遗传算法的特点，提出了一种融合遗传算法和BP算法的神经网络算法设计。该方法采用了基于实数编码的改进遗传算法来替代随机设定神经网络的初始权阈值，然后由改进的LMBP算法在已由遗传算法确定了的搜索空间中对网络进行精确训练。仿真结果表明神经网络的逼近能力和泛化能力得到了综合提高，能够有效抑制遗传算法初期收敛的发生，确保了快速达到全局收敛，克服了传统BP算法精度低、收敛速度慢、容易陷入局部极小的缺陷。     

Comparing performances of backpropagation and genetic algorithms in the data classification

Artificial neural networks (ANN) have a wide ranging usage area in the data classification problems. Backpropagation algorithm is classical technique used in the training of the artificial neural networks. Since this algorithm has many disadvantages, the training of the neural networks has been implemented with the binary and real-coded genetic algorithms. These algorithms can be used for the solutions of the classification problems. The real-coded genetic algorithm has been compared with other training methods in the few works. It is known that the comparison of the approaches is as important as proposing a new classification approach. For this reason, in this study, a large-scale comparison of performances of the neural network training methods is examined on the data classification datasets. The experimental comparison contains different real classification data taken from the literature and a simulation study. A comparative analysis on the real data sets and simulation data shows that the real-coded genetic algorithm may offer efficient alternative to traditional training methods for the classification problem.     

Cooperative binary-real coded genetic algorithms for generating and adapting artificial neural networks

This paper describes a genetic system for designing and training feed-forward artificial neural networks to solve any problem presented as a set of training patterns. This system, called GANN, employs two interconnected genetic algorithms that work parallelly to design and train the better neural network that solves the problem. Designing neural architectures is performed by a genetic algorithm that uses a new indirect binary codification of the neural connections based on an algebraic structure defined in the set of all possible architectures that could solve the problem. A crossover operation, known as Hamming crossover, has been designed to obtain better performance when working with this type of codification. Training neural networks is also accomplished by genetic algorithms but, this time, real number codification is employed. To do so, morphological crossover operation has been developed inspired on the mathematical morphology theory. Experimental results are reported from the application of GANN to the breast cancer diagnosis within a complete computer-aided diagnosis system.     

Genetic Algorithms for Solving a Class of Constrained Nonlinear Integer Programs

We consider a class of constrained nonlinear integer programs, which arise in manufacturing batch-sizing problems with multiple raw materials. In this paper, we investigate the use of genetic algorithms (GAs) for solving these models. Both binary and real coded genetic algorithms with six different penalty functions are developed. The real coded genetic algorithm works well for all six penalty functions compared to binary coding. A new method to calculate the penalty coefficient is also discussed. Numerical examples are provided and computational experiences are discussed.     

遗传算法优化前向神经网络结构和权重矢量

提出了新的遗传算法优化设计前向神经网络的结构和权重矢量。这种新方法的创新在于：二值码串和实值码串的混合编码方法即保留了传统遗传法的优点,又具有遗传编程和跗策略的优点。     

一种新的自适应量子遗传算法研究

传统的量子遗传算法是基于二进制编码进行的,每次计算需要进行编码和解码操作,影响了算法的效率。针对这一问题,提出了实数编码的自适应量子遗传算法（RQGA）。首先运用实数和量子比特共同编码,并采用自适应频率的临近算符对编码进行更新,而后运用自适应转角策略更新量子比特串,以保证算法保持搜索性能和求解性能的平衡。最后分别采用二进制遗传算法、二进制量子遗传算法以及实数和量子比特共同编码的自适应量子遗传算法对Schaffer’f6函数进行测试对比,结果表明,实数和量子比特共同编码的自适应量子遗传算法无论在收敛速度还是收敛精度方面都体现了较好的优越性。     

一种基于N维矩阵二值编码单亲遗传算法的QoS路由优化策略研究

文章提出了一种基于N维矩阵二值编码的单亲遗传算法解决计算机网络路由问题,仿真结果表明应用此种编码方式的单亲遗传算法进行路由选择不仅是可行和有效的,而且可以用于网络拓扑结构任意复杂网络的路由选择,此种单亲遗传算法比以往神经网络(NN)算法更优越。且与普通的遗传算法比较,不仅简化了遗传操作,实现容易,且不存在早熟收敛问题。     

A heuristic method for learning Bayesian networks using discrete particle swarm optimization

Bayesian networks are a powerful approach for representing and reasoning under conditions of uncertainty. Many researchers aim to find good algorithms for learning Bayesian networks from data. And the heuristic search algorithm is one of the most effective algorithms. Because the number of possible structures grows exponentially with the number of variables, learning the model structure from data by considering all possible structures exhaustively is infeasible. PSO (particle swarm optimization), a powerful optimal heuristic search algorithm, has been applied in various fields. Unfortunately, the classical PSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space. In this paper, two modifications of updating rules for velocity and position are introduced and a Bayesian networks learning based on binary PSO is proposed. Experimental results show that it is more efficient because only fewer generations are needed to obtain optimal Bayesian networks structures. In the comparison, this method outperforms other heuristic methods such as GA (genetic algorithm) and classical binary PSO.     

Quantum inspired evolutionary algorithm for ordering problems

This paper proposes a new quantum-inspired evolutionary algorithm for solving ordering problems. Quantum-inspired evolutionary algorithms based on binary and real representations have been previously developed to solve combinatorial and numerical optimization problems, providing better results than classical genetic algorithms with less computational effort. However, for ordering problems, order-based genetic algorithms are more suitable than those with binary and real representations. This is because specialized crossover and mutation processes are employed to always generate feasible solutions. Therefore, this work proposes a new quantum-inspired evolutionary algorithm especially devised for ordering problems (QIEA-O). Two versions of the algorithm have been proposed. The so-called pure version generates solutions by using the proposed procedure alone. The hybrid approach, on the other hand, combines the pure version with a traditional order-based genetic algorithm. The proposed quantum-inspired order-based evolutionary algorithms have been evaluated for two well-known benchmark applications – the traveling salesman problem (TSP) and the vehicle routing problem (VRP) – as well as in a real problem of line scheduling. Numerical results were obtained for ten cases (7 VRP and 3 TSP) with sizes ranging from 33 to 101 stops and 1 to 10 vehicles, where the proposed quantum-inspired order-based genetic algorithm has outperformed a traditional order-based genetic algorithm in most experiments.     

A memetic algorithm applied to the design of water distribution networks

The optimal design of water distribution networks is a real optimization problem that consists of finding the best way to convey water from the sources to the users, satisfying their requirements. Many researchers have reported algorithms for minimizing the network cost applying a large variety of techniques, such as linear programming, non-linear programming, global optimization methods and meta-heuristic approaches. However, a totally satisfactory and efficient method is not available as yet. Many works have assessed the performance of these techniques using small or medium-sized benchmark networks proposed in the literature, but few of them have tested these methods with large-scale real networks. This paper introduces a new memetic algorithm for the optimal design of water distribution networks. In order to establish an accurate conclusion, five other approaches have also been adapted, namely simulated annealing, mixed simulated annealing and tabu search, scatter search, genetic algorithms and binary linear integer programming. The results obtained in three water distribution networks show that the memetic algorithm performs better than the other methods, especially when the size of the problem increases.     

基于二进制位集合的遗传算法设计及应用

遗传算法中,基因型串结构数据表示为无符号二进制整数,但在传统程序设计中,数据中的“0”和“1”是以字符(char)形式存储的,这样表示的基因数据不仅在数据结构上和实际存在差异,多占用了内存空间,而且也使算法中的操作实际是字符串操作。利用C++面向对象设计思想,通过引入二进制位集合类,使基因型数据真正表示为二进制位(bit)数据,并改进了数据的存储方式,减少了内存需求,使遗传操作编程更方便。