临安市城乡供水一体化的供水能力确定

文章采用分项定额法，单位人口综合用水量指标法和基准年法分别计算，确定城乡供水的供水能力。     

基于改进粒子群算法的电力系统无功优化

电力系统无功优化问题是一个多变量、多约束的混合非线性规划问题。提出了一种改进粒子群算法用以解决这一复杂优化问题。在改进的算法中,首先结合混沌优化思想对粒子群进行初始化,减轻了粒子初始位置的选择对算法优化性能的影响;在进化过程中引入了自探索行为,使得粒子的搜索过程更加符合实际;引入了变异机制及3种判断陷入局部最优的标准,当发现粒子群陷入局部最优时,通过变异,帮助粒子跳出局部陷阱,增加发现最优解的机会。给出了问题的求解方法,并对IEEE 6、14节点系统进行了仿真计算,实验数值对比表明了算法的可行性和有效性。     

求解一般车辆优化调度问题的一种改进遗传算法

文章在对一般车辆优化调度问题(VRP)进行分析的基础上,为之建立了数学模型;提出了一种求解该问题的改进遗传算法,并通过实例验证了该算法具有优良性能。     

基于主动进化的遗传算法

根据当前遗传学和生物进化论中对变异方式的研究成果．将定向变异的思想引入到标准遗传算法领域，提出了一种基于主动进化的遗传算法．这种改进的遗传算法，可以在很大程度上克服现有遗传算法执行效率低的问题．我们将这种方法应用到TSP问题中，取得满意的实验结果、     

基于模糊逻辑控制器的自适应遗传算法

针对遗传算法中交叉概率和变异概率的缺陷，设计了一种基于模糊逻辑控制器的自适应遗传算法，实验结果表明，该算法具有较好的在线性能。     

基于变异概率自适应调整的逆序遗传算法研究

提出一种改进的遗传算法，根据个体适应度不同对变异概率进行自适应调整，使群体中的优良模式不易被破坏，同时又保证了种群个体的多样性，从而提高了算法的搜索效率。算法中改变了交叉与变异的操作顺序，避免了个体适应度的重复计算，提高运行速度。仿真结果表明，该算法优于普通遗传算法。     

Clone Selection Algorithm with Niching Strategy for Computer Immune System

A clone selection algorithm for computer immune system is presented. Clone selection principles in biological immune system are applied to the domain of computer virus detection. Based on the negative selection algorithm proposed by Stephanie Forrest, combining mutation operator in genetic algorithms and niching strategy in biology is adopted, the number of detectors is decreased effectively and the ability on self-nonself discrimination is improved. Simulation experiment shows that the algorithm is simple, practical and is adapted to the discrimination for long files.     

Finding Benefit in Breast Cancer During the Year After Diagnosis Predicts Better Adjustment 5 to 8 Years After Diagnosis.

Cancer patients experience positive as well as adverse consequences from diagnosis and treatment. The study reported here examined longer term reverberations of such experiences. A set of benefit-finding items along with measures of well-being were completed by 230 early-stage breast cancer patients in the year postsurgery. Four to 7 years later, 96 of them again completed measures of well-being. Controlling for initial distress and depression, initial benefit finding in this sample predicted lower distress and depression at follow-up. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using differential evolution for fine tuning naïve Bayesian classifiers and its application for text classification

The Naive Bayes (NB) learning algorithm is simple and effective in many domains including text classification. However, its performance depends on the accuracy of the estimated conditional probability terms. Sometimes these terms are hard to be accurately estimated especially when the training data is scarce. This work transforms the probability estimation problem into an optimization problem, and exploits three metaheuristic approaches to solve it. These approaches are Genetic Algorithms (GA), Simulated Annealing (SA), and Differential Evolution (DE). We also propose a novel DE algorithm that uses multi-parent mutation and crossover operations (MPDE) and three different methods to select the final solution. We create an initial population by manipulating the solution generated by a method used for fine tuning the NB. We evaluate the proposed methods by using their resulted solutions to build NB classifiers and compare their results with the results of obtained from classical NB and Fine-Tuning Naïve Bayesian (FTNB) algorithm, using 53 UCI benchmark data sets. We name these obtained classifiers NBGA, NBSA, NBDE, and NB-MPDE respectively. We also evaluate the performance NB-MPDE for text-classification using 18 text-classification data sets, and compare its results with the results of obtained from FTNB, BNB, and MNB. The experimental results show that using DE in general and the proposed MPDE algorithm in particular are more convenient for fine-tuning NB than all other methods, including the other two metaheuristic methods (GA, and SA). They also indicate that NB-MPDE achieves superiority over classical NB, FTNB, NBDE, NBGA, NBSA, MNB, and BNB.     

Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington disease

More than 20 human genetic diseases are associated with inheriting an unstable expanded DNA simple sequence tandem repeat, for example, CTG (cytosine–thymine–guanine) repeats in myotonic dystrophy type 1 (DM1) and CAG (cytosine–adenine–guanine) repeats in Huntington disease (HD). These sequences mutate by changing the number of repeats not just between generations, but also during the lifetime of affected individuals. Levels of somatic instability contribute to disease onset and progression but as changes are tissue-specific, age- and repeat length-dependent, interpretation of the level of somatic instability in an individual is confounded by these considerations. Mathematical models, fitted to CTG repeat length distributions derived from blood DNA, from a large cohort of DM1-affected or at risk individuals, have recently been used to quantify inherited repeat lengths and mutation rates. Taking into account age, the estimated mutation rates are lower than predicted among individuals with small alleles (inherited repeat lengths less than 100 CTGs), suggesting that these rates may be suppressed at the lower end of the disease-causing range. In this study, we propose that a length-specific effect operates within this range and tested this hypothesis using a model comparison approach. To calibrate the extended model, we used data derived from blood DNA from DM1 individuals and, for the first time, buccal DNA from HD individuals. In a novel application of this extended model, we identified individuals whose effective repeat length, with regards to somatic instability, is less than their actual repeat length. A plausible explanation for this distinction is that the expanded repeat tract is compromised by interruptions or other unusual features. We quantified effective length for a large cohort of DM1 individuals and showed that effective length better predicts age of onset than inherited repeat length, thus improving the genotype–phenotype correlation. Under the extended model, we removed some of the bias in mutation rates making them less length-dependent. Consequently, rates adjusted in this way will be better suited as quantitative traits to investigate cis- or trans-acting modifiers of somatic mosaicism, disease onset and progression.