附加的 赚钱的

对于每一个人来说,“小人”都是一个极端的蔑称。所谓“两面三刀、阳奉阴违”,人人都敬而远之,古龙在其书中说到,他最痛恨的就是“嘴里叫哥哥,手里捅刀子”的家伙。猫王的一位朋友就曾有感而发:“千万不要轻信那些表面的现象,也不要被那些甜言蜜语所迷惑,不了解实际情况决不能轻易下手。那些奸商……”朋友很是愤怒,因为他受过骗。那年,他要买打印机,“就想买个耐用的,便宜点的。”于是,这老兄四处查找信息,终于在某杂志上找到他所中意的对象,便宜、耐用、不偏不倚。朋友打电话去询问,销售小姐很热情,各项情况娓娓道来,并保证广告所说一切属实,敬请现场查看。朋友一时喜不自禁,第二天就驱车前     

适合的才是好的

任何一个产业,在刚刚起步阶段,国家的保护是至关重要的,这会促进这个产业的快速发展,但我们不能始终躺在国家庇护的树荫下乘凉,要主动去适应市场。     

极致的的美丽

2007年到2008年是第四次国际极地年,有关保护环境,保护地球的理论说教,我不想继续。我们奉献给读者的是这样一组美轮美奂的极地作品。对于这样极致的美丽,你还忍心去破坏它,去摧毁它吗？     

合适的才是好的

夏天的天气简直熱得都快要赶上e浪潮了,让e诸葛实在有点不受用。于是乎,e诸葛抽了个空到大商场去查探了一番。虽然这不是一个大老爷们愿意干的事,但是既然是“微服私访”,就有几个原因:一是想吹吹空调,吸吸凉气,驱走烦躁;二是想在商场中找寻一些关于传统零售业迟迟不肯进入电子商务的蛛丝马迹。走了走大商场.他好象明白了其中一二。都说“购物是一种享受”,大概只有抱着研究的心态,e诸葛才注意到:商场中不但有凉爽的环境、柔和的灯光、优美的音乐,就连货物的摆放、位置也有讲究。似乎大型商场一层总是化妆品和首饰,二、三层铁定是男装女装,超市嘛,肯定在地下……这就是传统商家的一些经验吧。有层次感、丰富多样的物品加上员工的服务态度……加在一起     

小的才是好的

前年秋天,我奉学校之派,到韩国圆光大学任教.刚刚跨入大学为我安排的宿舍,就发现写字桌上放着一台半新不旧的电脑.这可真是喜出望外.在家里我用惯了电脑,这一下可免除了无“脑”的烦恼.一喜之下,我马上又关心起圆光大学的网络设施问题.能不能方便快捷地上因特网,这可关系我在韩国的生活质量.因为身在异国,中文的报刊杂志少而又少.好不容易看到《人民日报》海外版,往往是个把星期以前的旧闻.只要能上网,看看国内的网上报章,无疑会解除文化信息的干渴.     

穿过你的防火墙的我的手

在网上和别人用QQ聊天时,如何能够绕过防火墙和别人传递文件呢?试过FileGateway,你就会有答案了。     

灵活的配置软件系统的文件目录的方法

介绍了配置软件系统文件目录的重要性以及3种配置方式,在此基础上提出了在固定式目录和相对式目录下实现灵活配置的方法,比较了两种方法的优缺点.得出结论：在大型的软件系统中两种方法结合使用;在小型软件系统中一般采用相对式目录.     

飘过我的心情的你的网

初遇Hero,是在I社区里,那时候自己刚到这家公司,对各种图片软件一窍不通,而主编交给我的一大堆任务全跟那些PHOTOSHOP,DREAMWEAVER什么的有关,我看着它们,这里改改,那里改改,都不得要领,简直被他们弄得焦头烂额了。于是百无聊赖之际,走走停停地就来到I社区,开始以为这不过是个供人扯淡的茶庄,谁知才说了一两句话就发现这里高手如     

求解矩形Packing问题的基于遗传算法的启发式递归策略

An improved heuristic recursive strategy combining with genetic algorithm is presented in this paper. Firstly, this method searches some rectangles, which have the same length or width, to form some layers without waste space, then it uses the heuristic recursive strategies to calculate the height of the remaining packing order and uses the evolutionary capability of genetic algorithm to reduce the height. The computational results on several classes of benchmark problems have shown that the presented algorithm can compete with known evolutionary heuristics. It performs better especially for large test problems.     

求解矩形装箱问题的一种近似算法

提出了利用近似算法求解二维矩形装箱问题的最小高度的一种方法。该方法基于启发式递归策略和遗传算法。利用启发式递归策略把所有大小各异的矩形都装入宽度固定的矩形容器中，并计算装完后所需容器的高度，用遗传算法的进化能力优化高度，使得所需容器的高度尽可能小。计算数据证明这种方法能够得到很好的结果，特别是对数据量大的测试问题，效果更好。     

C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

In recent years, evolutionary algorithms (EAs) have been extensively developed and utilized to solve multi-objective optimization problems. However, some previous studies have shown that for certain problems, an approach which allows for non-greedy or uphill moves (unlike EAs), can be more beneficial. One such approach is simulated annealing (SA). SA is a proven heuristic for solving numerical optimization problems. But owing to its point-to-point nature of search, limited efforts has been made to explore its potential for solving multi-objective problems. The focus of the presented work is to develop a simulated annealing algorithm for constrained multi-objective problems. The performance of the proposed algorithm is reported on a number of difficult constrained benchmark problems. A comparison with other established multi-objective optimization algorithms, such as infeasibility driven evolutionary algorithm (IDEA), Non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective Scatter search II (MOSS-II) has been included to highlight the benefits of the proposed approach.     

A new evolutionary approach to cutting stock problems with and without contiguity

Evolutionary algorithms (EAs) have been applied to many optimization problems successfully in recent years. The genetic algorithm (GAs) and evolutionary programming (EP) are two different types of EAs. GAs use crossover as the primary search operator and mutation as a background operator, while EP uses mutation as the primary search operator and does not employ any crossover. This paper proposes a novel EP algorithm for cutting stock problems with and without contiguity. Two new mutation operators are proposed. Experimental studies have been carried out to examine the effectiveness of the EP algorithm. They show that EP can provide a simple yet more effective alternative to GAs in solving cutting stock problems with and without contiguity. The solutions found by EP are significantly better (in most cases) than or comparable to those found by GAs.Scope and purposeThe one-dimensional cutting stock problem (CSP) is one of the classical combinatorial optimization problems. While most previous work only considered minimizing trim loss, this paper considers CSPs with two objectives. One is the minimization of trim loss (i.e., wastage). The other is the minimization of the number of stocks with wastage, or the number of partially finished items (pattern sequencing or contiguity problem). Although some traditional OR techniques (e.g., programming based approaches) can find the global optimum for small CSPs, they are impractical to find the exact global optimum for large problems due to combinatorial explosion. Heuristic techniques (such as various hill-climbing algorithms) need to be used for large CSPs. One of the heuristic algorithms which have been applied to CSPs recently with success is the genetic algorithm (GA). This paper proposes a much simpler evolutionary algorithm than the GA, based on evolutionary programming (EP). The EP algorithm has been shown to perform significantly better than the GA for most benchmark problems we used and to be comparable to the GA for other problems.     

求解矩形Packing问题的砌墙式启发式算法

为求解正交矩形Packing问题提出了一个新颖而有效的砌墙式启发式算法.该算法主要基于砌墙式启发式策略,其思想主要来源于砖匠在砌墙过程中所积累的经验:基于基准砖的砌墙规则.对国际上公认的大量的Bench-mark问题例的计算结果表明,该算法的计算速度不仅比著名的现代启发式算法快,而且获得更优的高度.     

不确定环境下一种新的双蚁群路径规划算法

蚁群算法作为一种新型的模拟进化算法,被广泛地用于路径规划问题。但是传统的蚁群算法存在搜索时间长、收敛速度慢、易于陷入局部最优等缺点,为了克服算法的不足,该文提出一种改进的双蚁群算法,通过改变启发因子,同时引入最大最小蚁群系统思想对信息素进行更新以提高算法性能。实验结果表明,与同类算法相比,该算法能得到更优的路径。     

一种从海量不完备决策表中抽取规则的方法

提出了一种处理海量的不完备决策表的方法。将基于互信息的属性重要度作为启发式信息,利用遗传算法对不完备的原始决策表中的条件属性进行约简,形成包含missing值的决策表,称为优化决策表。利用原始决策表自身的信息,通过属性扩展,从优化决策表中抽取一致性决策规则,而无须计算missing值。该方法在UCI的8个数据集上的实验结果优于EMAV方法,是一种有效的从海量不完备决策表中抽取规则的方法。     

A two-leveled symbiotic evolutionary algorithm for clustering problems

Because of its unsupervised nature, clustering is one of the most challenging problems, considered as a NP-hard grouping problem. Recently, several evolutionary algorithms (EAs) for clustering problems have been presented because of their efficiency for solving the NP-hard problems with high degree of complexity. Most previous EA-based algorithms, however, have dealt with the clustering problems given the number of clusters (K) in advance. Although some researchers have suggested the EA-based algorithms for unknown K clustering, they still have some drawbacks to search efficiently due to their huge search space. This paper proposes the two-leveled symbiotic evolutionary clustering algorithm (TSECA), which is a variant of coevolutionary algorithm for unknown K clustering problems. The clustering problems considered in this paper can be divided into two sub-problems: finding the number of clusters and grouping the data into these clusters. The two-leveled framework of TSECA and genetic elements suitable for each sub-problem are proposed. In addition, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. The performance of the proposed algorithm is compared with some popular evolutionary algorithms using the real-life and simulated synthetic data sets. Experimental results show that TSECA produces more compact clusters as well as the accurate number of clusters.     

An Integrated Technique for Task Matching and Scheduling onto Distributed Heterogeneous Computing Systems

This paper presents a problem-space genetic algorithm (PSGA)-based technique for efficient matching and scheduling of an application program that can be represented by a directed acyclic graph, onto a mixed-machine distributed heterogeneous computing (DHC) system. PSGA is an evolutionary technique that combines the search capability of genetic algorithms with a known fast problem-specific heuristic to provide the best-possible solution to a problem in an efficient manner as compared to other probabilistic techniques. The goal of the algorithm is to reduce the overall completion time through proper task matching, task scheduling, and inter-machine data transfer scheduling in an integrated fashion. The algorithm is based on a new evolutionary technique that embeds a known problem-specific fast heuristic into genetic algorithms (GAs). The algorithm is robust in the sense that it explores a large and complex solution space in smaller CPU time and uses less memory space as compared to traditional GAs. Consequently, the proposed technique schedules an application program with a comparable schedule length in a very short CPU time, as compared to GA-based heuristics. The paper includes a performance comparison showing the viability and effectiveness of the proposed technique through comparison with existing GA-based techniques.     

Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique

A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive performance with respect to some other state-of-the-art approaches in constrained evolutionary optimization.