基于差异进化的克隆选择算法

针对免疫算法在全局优化过程中多样性不足的问题,将差异进化引入克隆变异操作中,提出了一个新的改进的克隆选择算法——基于差异进化的克隆选择算法(DECSA),算法将差异进化和克隆超变异相结合,促进了抗体与抗体之间的信息融合,使得子代抗体继承父代抗体的信息的同时,携带着不同父代个体信息,丰富了抗体种群的多样性,实现了在同一父代抗体周围的多个方向同时进行全局和局部搜索。对13个标准测试函数的测试结果及与已有的算法的比较表明,该算法表现出较好的局部搜索和全局搜索能力。     

On the use of energy minimization for CA based analysis in elasticity

There has been recent interest in exploring alternative computational models for structural analysis that are better suited for a design environment requiring repetitive analysis. The need for such models is brought about by significant increases in computer processing speeds, realized primarily through parallel processing. To take full advantage of such parallel machines, however, the computational approach itself must be revisited from a totally different perspective; parallelization of inherently serial paradigms is subject to limitations introduced by a requirement of information coordination. The cellular automata (CA) model of decentralized computations provides one such approach which is ideally tailored for parallel computers. The proposed paper examines the applicability of the cellular automata model in problems of 2-D elasticity. The focus of the paper is in the use of a genetic algorithm based optimization process to derive the rules for local interaction required in evolving the cellular automata. Received August 28, 2000     

Neural Network Method for Solving Partial Differential Equations

A method is presented to solve partial differential equations (pde's) and its boundary and/or initial conditions by using neural networks. It uses the fact that multiple input, single output, single hidden layer feedforward networks with a linear output layer with no bias are capable of arbitrarily well approximating arbitrary functions and its derivatives, which is proven by a number of authors and well known in literature. Knowledge about the pde and its boundary and/or initial conditions is incorporated into the structures and the training sets of several neural networks. In this way we obtain networks of which some are specifically structured. To find the solution of the pde and its boundary and/or initial conditions we have to train all obtained networks simultaneously. Therefore we use an evolutionary algorithm to train the networks. We demonstrate the working of our method by applying it to two problems.     

An evolutionary approach to select a pull system among Kanban, Conwip and Hybrid

This paper describes a methodology for the choice of a pull production-control strategy. The methodology is based on optimization, using an Evolutionary Algorithm and discrete-event simulation, of a generic system that can model Kanban, Conwip, and Hybrid. This approach is illustrated through the examples of production lines with six, eight, and ten stages. The optimization procedure leads to a simplified Hybrid system.     

RBF网学习的进化优选算法

讨论了用正交最小二乘算法训练ＲＢＦ网的不足之处,然后引入了选择路径的概念,在此基础上,提出了ＲＢＦ网隐层节点选取的进化优选算法,仿真结果表明,在不同的精度要求下,用进化优选算法均能设计出比正交最小二乘算法更小的ＲＢＦ网。     

基于辨识标签原理的无源声表面波传感器技术要点与发展趋势

本文在讨论基于辨识标签原理的无源声表面波传感器的原理的基础上,分析了衰减损耗和尺寸、交叉敏感、反射极排列和信号处理等传感器技术要点的当前存在问题以及发展趋势。     

Web日志挖掘与上网行为习惯分析

本文以某校校园网Web日志挖掘为例,首先阐述如何进行Web挖掘,其次运用该理论分析该校Web日志得到用户上网行为习惯,并给出合理化建议,最后引出进一步探索的方向,为以后工作奠定基础。本文采用的挖掘方法具有通用性和实用性,对于类似时序序列趋势分析都可参考本文。     

Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge

This paper presents an analysis of the performance of the (μ/μ,λ)-ES with isotropically distributed mutations and cumulative step length adaptation on the noisy parabolic ridge. Several forms of dependency of the noise strength on the distance from the ridge axis are considered. Closed form expressions are derived that describe the mutation strength and the progress rate of the strategy in high-dimensional search spaces. It is seen that as for the sphere model, larger levels of noise present lead to cumulative step length adaptation generating increasingly inadequate mutation strengths, and that the problem can be ameliorated to some degree by working with larger populations.     

Incremental Evolution in ANNs: Neural Nets which Grow

This paper explains the optimisation of neuralnetwork topology using Incremental Evolution;that is, by allowing the network to expand byadding to its structure. This method allows anetwork to grow from a simple to a complexstructure until it is capable of fulfilling itsintended function. The approach is somewhatanalogous to the growth of an embryo or theevolution of a fossil line through time, it istherefore sometimes referred to as anembryology or embryological algorithm. Thepaper begins with a general introduction,comparing this method to other competingtechniques such as The Genetic Algorithm, otherEvolutionary Algorithms and SimulatedAnnealing. A literature survey of previous workis included, followed by an extensive newframework for application of the technique.Finally, examples of applications and a generaldiscussion are presented.     

Optimizing Epochal Evolutionary Search: Population-Size Dependent Theory

Epochal dynamics, in which long periods of stasis in an evolving population are punctuated by a sudden burst of change, is a common behavior in both natural and artificial evolutionary processes. We analyze the population dynamics for a class of fitness functions that exhibit epochal behavior using a mathematical framework developed recently, which incorporates techniques from the fields of mathematical population genetics, molecular evolution theory, and statistical mechanics. Our analysis predicts the total number of fitness function evaluations to reach the global optimum as a function of mutation rate, population size, and the parameters specifying the fitness function. This allows us to determine the optimal evolutionary parameter settings for this class of fitness functions.We identify a generalized error threshold that smoothly bounds the two-dimensional regime of mutation rates and population sizes for which epochal evolutionary search operates most efficiently. Specifically, we analyze the dynamics of epoch destabilization under finite-population sampling fluctuations and show how the evolutionary parameters effectively introduce a coarse graining of the fitness function. More generally, we find that the optimal parameter settings for epochal evolutionary search correspond to behavioral regimes in which the consecutive epochs are marginally stable against the sampling fluctuations. Our results suggest that in order to achieve optimal search, one should set evolutionary parameters such that the coarse graining of the fitness function induced by the sampling fluctuations is just large enough to hide local optima.