Experiences and issues in evaluating tool requirements using genetic algorithms

This paper is concerned with the optimization of tool requirements in flexible manufacturing systems (FMSs). The main aim is to determine the best tool spectrum (i.e. the number of duplicates for each tool type to be provided) considering different layouts of the tool management area (tool rooms). The optimization method is based on the reciprocal interaction between a genetic algorithm (GA) and the simulation model of the FMS considered. Thus, the results proposed refer to the efficacy of the technique adopted and its practical application to tool management.     

A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing

This paper investigates an integrated production and transportation scheduling problem in an MTO supply chain. A harmony search-based memetic optimization model is developed to handle this problem, in which certain heuristic procedures are proposed to convert the investigated problem into an order assignment problem. A novel improvisation process is also proposed to improve the optimum-seeking performance. The effectiveness of the proposed model is validated by numerical experiments. The experimental results show that (1) the proposed model can solve the investigated problem effectively and that (2) the proposed memetic optimization process exhibits better optimum-seeking performance than genetic algorithm-based and traditional memetic optimization processes.     

皮亚杰“儿童自我中心主义”概念评析

在皮亚杰的发生认识论学说中,“儿童自我中心”概念具有非常重要的意义。“儿童自我中心主义”表现在两个方面,即理智的自我中心和社会的自我中心。同时也表现在儿童心理发展的两个不同阶段,即感知-运动阶段和前运算阶段。皮亚杰指出:话动缺乏协调、主客体没有分化是心理上的自我中心状态的根源。儿童理智上的和社会的自我中心都是随着动作的逐渐协调而消逝的。如果仅从个体心理的基础来看,皮亚杰对儿童自我中心状态的根源的分析,以及他所指出的去除自我中心的途径,是基本正确的。但从社会心理机制的角度看,则存在一些明显的局限:明显的认知中心论,混淆了主客体关系与主体间关系,忽略了符号功能与摆脱社会的自我中心状态的内在联系。     

Clustering and combinatorial optimization in recursive supervised learning

The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In this chapter, we revise the global search component by replacing it with a cluster based combinatorial optimization. Patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore created, which is solved using evolutionary algorithms. The evolutionary algorithms identify the “easy” and the “difficult” clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. Empirical studies show generally good performance when compared to other state of the art methods.     

供应链二级分销网络的优化研究

在综合考虑各种费用要素的基础上,提出了供应链二级分销网络优化模型,该模型属于一类选址-分配问题,文章采用遗传算法对其进行求解,从中选出预建立的分销中心,然后采用蚂蚁算法对建立起的二级网络优化运输路线,从而使得总成本更小,文章中用具体的例子进行了仿真,验证了优化模型的正确性和算法的有效性。     

基于Rough集的属性约简的算法分析

研究了粗集理论中基于差别矩阵,关联矩阵的属性约简算法,分析了各种算法和原理,指出了优缺点,对以后的研究方向提出了建议.     

安徽省市域经济增长质量动态模糊综合评价——基于改进的AHP-FCE集成模型

基于评价指标提供的信息和运用改进的AHP-FCE集成模型对2002—2011年间安徽省各市经济增长质量进行了动态模糊综合评价。从构建的描述经济增长质量5个维度的指标体系中选取了8个代表性指标,建立模糊关系矩阵并优化生成客观权重,由模糊合成运算得到动态综合评价结果。通过各市经济增长质量动态综合评价值、最大序差和影响因素分析,显示出安徽省市域经济增长质量在10年间的动态变化轨迹和区域性差异,只有个别城市处于经济增长质量的核心区域,更多的城市则处于经济增长质量的＂跳跃＂区域,经济增长质量的各维度发展程度不均衡。     

A robust automatic clustering algorithm for probability density functions with application to categorizing color images

This study develops a robust automatic algorithm for clustering probability density functions based on the previous research. Unlike other existing methods that often pre-determine the number of clusters, this method can self-organize data groups based on the original data structure. The proposed clustering method is also robust in regards to noise. Three examples of synthetic data and a real-world COREL dataset are utilized to illustrate the accurateness and effectiveness of the proposed approach.     

Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data

In this work, we assume that the sequence recording whether or not an ozone exceedance of an environmental threshold has occurred in a given day is ruled by a non-homogeneous Markov chain of order one. In order to account for the possible presence of cycles in the empirical transition probabilities, a parametric form incorporating seasonal components is considered. Results show that even though some covariates (namely, relative humidity and temperature) are not included explicitly in the model, their influence is captured in the behavior of the transition probabilities. Parameters are estimated using the Bayesian point of view via Markov chain Monte Carlo algorithms. The model is applied to ozone data obtained from the monitoring network of Mexico City, Mexico. An analysis of how the methodology could be used as an aid in the decision-making is also given.     

Comparison of computer programs for simple linear L 1 regression

A number of efficient computer codes are available for the simple linear L ₁ regression problem. However, a number of these codes can be made more efficient by utilizing the least squares solution. In fact, a couple of available computer programs already do so.

We report the results of a computational study comparing several openly available computer programs for solving the simple linear L ₁ regression problem with and without computing and utilizing a least squares solution.