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1.
High-order neural network structure selection for function approximation applications using genetic algorithms 总被引:3,自引:0,他引:3
Rovithakis G.A. Chalkiadakis I. Zervakis M.E. 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2004,34(1):150-158
Neural network literature for function approximation is by now sufficiently rich. In its complete form, the problem entails both parametric (i.e., weights determination) and structural learning (i.e., structure selection). The majority of works deal with parametric uncertainty assuming knowledge of the appropriate neural structure. In this paper we present an algorithmic approach to determine the structure of High Order Neural Networks (HONNs), to solve function approximation problems. The method is based on a Genetic Algorithm (GA) and is equipped with a stable update law to guarantee parametric learning. Simulation results on an illustrative example highlight the performance and give some insight of the proposed approach. 相似文献
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Evolutionary Radial Basis Functions for Credit Assessment 总被引:1,自引:1,他引:0
Credit analysts generally assess the risk of credit applications based on their previous experience. They frequently employ quantitative methods to this end. Among the methods used, Artificial Neural Networks have been particularly successful and have been incorporated into several computational tools. However, the design of efficient Artificial Neural Networks is largely affected by the definition of adequate values for their free parameters. This article discusses a new approach to the design of a particular Artificial Neural Networks model, RBF networks, through Genetic Algorithms. It presents an overall view of the problems involved and the different approaches employed to optimize Artificial Neural Networks genetically. For such, several methods proposed in the literature for optimizing RBF networks using Genetic Algorithms are discussed. Finally, the model proposed by the authors is described and experimental results using this model for a credit risk assessment problem are presented. 相似文献
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单亲遗传算法及其全局收敛性分析 总被引:77,自引:0,他引:77
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. 相似文献
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Fredholm Integral Equation of the First Kind (FIEFK) is an example of ill-posed problems. Solving this type of equation using
conventional methods of discretization often leads to an ill-conditioned system of linear equations. This paper deals with
the numerical solution for the FIEFK occurring in the synthesis of the electromagnetic fields. To tackle this problem, we
propose a hybrid method based on Genetic Algorithms (GAs) and Artificial Neural Networks. The method consists of two major
steps. The first step is to find an initial solution by utilizing a GA, and the second is to refine the solution using a regularized
neural network. Experimental results prove the efficiency of our proposed method in comparison with a previous work. 相似文献
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Minimum spanning tree (MST) problem is of high importance in network optimization and can be solved efficiently. The multi-criteria MST (mc-MST) is a more realistic representation of the practical problems in the real world, but it is difficult for traditional optimization technique to deal with. In this paper, a non-generational genetic algorithm (GA) for mc-MST is proposed. To keep the population diversity, this paper designs an efficient crossover operator by using dislocation a crossover technique and builds a niche evolution procedure, where a better offspring does not replace the whole or most individuals but replaces the worse ones of the current population. To evaluate the non-generational GA, the solution sets generated by it are compared with solution sets from an improved algorithm for enumerating all Pareto optimal spanning trees. The improved enumeration algorithm is proved to find all Pareto optimal solutions and experimental results show that the non-generational GA is efficient. 相似文献
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面向对象的遗传算法及其在神经网络辅助设计中的应用 总被引:2,自引:0,他引:2
在现有的遗传算法的基础上,采用面向对象技术设计了面向对象的遗传算法,建立了遗传算法的类层次。这种方法改变了在传统的遗传算法中各个函数之间只有参数的传递,而没有代码的继承性的状况从概念上提高了软件的可重用性。该方法在人工神经网络的辅助设计问题中的应用表明,这一算法由于采用面向对象的分析与设计方法,从而具有比传统的遗传算法更好的通用性,用户可以更方便地设计和实现自己的编码方案和遗传算子,大大提高了软件的可重用性。 相似文献
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In customized mass production, isolation of Process Planning (PP) and Scheduling stages has a critical effect on the efficiency of production. In this study, to overcome this isolation problem, we propose an integrated system that does PP and Scheduling in parallel and responds to fluctuations in job floor on time. One common problem observed in integration models is the increase in computational time in conjunction with the increase of problem size. Therefore in this study, we use a hybrid heuristic model combining both Genetic Algorithm (GA) and Fuzzy Neural Network (FNN). To improve GA performance and increase the efficiency of searching, we use a clustered chromosome structure and test the performance of GA with respect to different scenarios. Data provided by GA is used in constructing an FNN model that instantly provides new schedules as new constraints emerge in the production environment. Introduction of fuzzy membership functions in Artificial Neural Network (ANN) model allows us to generate fuzzy rules for production environment. 相似文献
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《Computer Methods in Applied Mechanics and Engineering》2005,194(36-38):3749-3770
In this paper, a bit-array representation method for structural topology optimization using the Genetic Algorithm (GA) is implemented. The importance of structural connectivity in a design is further emphasized by considering the total number of connected objects of each individual explicitly in an equality constraint function. To evaluate the constrained objective function, Deb’s constraint handling approach is further developed to ensure that feasible individuals are always better than infeasible ones in the population to improve the efficiency of the GA. A violation penalty method is proposed to drive the GA search towards the topologies with higher structural performance, less unusable material and fewer separate objects in the design domain. An identical initialization method is also proposed to improve the GA performance in dealing with problems with long narrow design domains. Numerical results of structural topology optimization problems of minimum weight and minimum compliance designs show the success of this bit-array representation method and suggest that the GA performance can be significantly improved by handling the design connectivity properly. 相似文献
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Multi-constrained routing (MCR) aims to find the feasible path in the network that satisfies multiple independent constraints, it is usually used for routing multimedia traffic with quality-of-service (QoS) guarantees. It is well known that MCR is NP-complete. Heuristic and approximate algorithms for MCR are not effective in dynamic network environment for real-time applications when the state information of the network is out of date. This paper presents a genetic algorithm to solve the MCR problem subject to transmission delay and transmission success ratio. Three key design problems are investigated for this new algorithm, i.e., how to encode the problem in genetic representation, how to avoid the illegal chromosomes in the process of population initialization and genetic operation, and how to design effective genetic operator. We propose the gene structure (GS) to deal with the first problem, and the gene structure algorithm (GSA) to generate the GS. Based on the GS, we provide the heuristic chromosome initialization and mutation operator to solve the last two problems. Computer simulations show that the proposed GA exhibits much faster computation speed so as to satisfy the real-time requirement, and much higher rate of convergence than other algorithms. The results are relatively independent of problem types (network scales and topologies). Furthermore, simulation results show that the proposed GA is effective and efficient in dynamic network environment. 相似文献
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一种改进选择算子的遗传算法 总被引:2,自引:1,他引:1
遗传算法(Genetic Algorithm,GA)是一种模拟生物进化的智能算法,被广泛应用于求解各类问题。简单遗传算法(Simple GA)仅靠变异产生新的数值,常常存在搜索精确度不高的问题。针对这个问题,对SGA的选择算子进行改进,即把相似个体分在同一组中,以组为单位进行选择,并通过该组个体的特点进行高斯搜索生成新的群体。这样使得GA在搜索过程中不仅可以很好地保持个体的多样性,并且可以提高解的精确度。通过对11个函数(单峰和多峰)的仿真实验,证明了采用新的选择算子后,GA在求解问题的精确度上有了很大地改善。 相似文献
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Graph Coloring Problems (GCPs) are constraint optimization problems with various applications including time tabling and frequency allocation. The GCP consists in finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. We propose a hierarchical approach based on Parallel Genetic Algorithms (PGAs) to solve the GCP. We call this new approach Hierarchical PGAs (HPGAs). In addition, we have developed a new operator designed to improve PGAs when solving constraint optimization problems in general and GCPs in particular. We call this new operator Genetic Modification (GM). Using the properties of variables and their relations, GM generates good individuals at each iteration and inserts them into the PGA population in the hope of reaching the optimal solution sooner. In the case of the GCP, the GM operator is based on a novel Variable Ordering Algorithm (VOA) that we propose. Together with the new crossover and the estimator of the initial solution we have developed, GM allows our solving approach to converge towards the optimal solution sooner than the well known methods for solving the GCP, even for hard instances. This was indeed clearly demonstrated by the experiments we conducted on the GCP instances taken from the well known DIMACS website. 相似文献
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《Artificial Intelligence in Engineering》1999,13(4):367-372
This article describes the application of Machine Learning (ML) techniques to a real world problem: the Automatic Diagnosis (classification) of Mammary Biopsy Images. The techniques applied are Genetic Algorithms (GA) and Case-Based Reasoning (CBR). The article compares our results with previous results obtained using Neural Networks (NN). The main goals are: to efficiently solve classification problems of such a type and to compare different alternatives for Machine Learning. The article also introduces the systems we developed for solving this kind of classification problems: Genetic Based Classifier System (GeB-CS) for a GA approach, and Case-Based Classifier System (CaB-CS) for a CBR approach. 相似文献
17.
The evolutionary algorithms are extensively adopted to resolve complex optimization problem. Genetic algorithm (GA), an evolutionary
algorithm, has been proved capable of solving vehicle routing problems (VRPs). However, the resolution effectiveness of GA
decreases with the increase of nodes within VRPs. Normally, a hybrid GA outperforms pure GA. This study attempts to solve
a capacitated vehicle routing problem (CVRP) by applying a novel hybrid genetic algorithm (HGA) that is practical for use
by manufacturers. The proposed HGA involves three stages. First, a diverse and well-structured initial chromosome population
was constructed. Second, response surface methodology (RSM) experiments were conducted to optimize the crossover and mutation
probabilities in performing GA. Finally, a combined heuristics containing improved insertion algorithm and random insertion
mutation operator was established to stir over gene permutations and enhance the exploration capability of GA diversely. Furthermore,
an elitism conservation strategy was implemented that replace inferior chromosomes with superior ones. As the proposed HGA
is primarily used to solve practical problems, benchmark problems involving fewer than 100 nodes from an Internet website
were utilized to confirm the feasibility of the proposed HGA. Two real cases one for locally active distribution and another
for arms part transportation at a combined maintenance facility, both involving the Taiwanese armed forces are used to detail
the analytical process and demonstrate the practicability of the proposed HGA for optimizing the CVRP. 相似文献
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Spam is a serious universal problem which causes problems for almost all computer users. This issue affects not only normal users of the internet, but also causes a big problem for companies and organizations since it costs a huge amount of money in lost productivity, wasting users’ time and network bandwidth. Many studies on spam indicate that spam cost organizations billions of dollars yearly. This work presents a machine learning method inspired by the human immune system called Artificial Immune System (AIS) which is a new emerging method that still needs further exploration. Core modifications were applied on the standard AIS with the aid of the Genetic Algorithm. Also an Artificial Neural Network for spam detection is applied with a new manner. SpamAssassin corpus is used in all our simulations. 相似文献
19.
Derivative and GA-based methods in metamodeling of back-propagation neural networks for constrained approximate optimization 总被引:1,自引:0,他引:1
Jongsoo Lee Heeseok Jeong Seongkyu Kang 《Structural and Multidisciplinary Optimization》2008,35(1):29-40
Artificial neural networks (ANN) have been extensively used as global approximation tools in the context of approximate optimization.
ANN traditionally minimizes the absolute difference between target outputs and approximate outputs thereby resulting in approximate
optimal solutions being sometimes actually infeasible when it is used as a metamodel for inequality constraint functions.
The paper explores the development of the efficient back-propagation neural network (BPN)-based metamodel that ensures the
constraint feasibility of approximate optimal solution. The BPN architecture is optimized via two approaches of both derivative-based
method and genetic algorithm (GA) to determine interconnection weights between layers in the network. The verification of
the proposed approach is examined by adopting a standard ten-bar truss problem. Finally, a GA-based approximate optimization
of suspension with an optical flying head is conducted to enhance the shock resistance capability in addition to dynamic characteristics. 相似文献