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1、 Performance assessment of multi-objective optimizersWith many multi-objective optimization problems,knowledge about the Pareto-optimal front helps the decision maker in choosing the best compromise solution. For instance, when designing computer systems,engineers often perform a so-called design sp
2、ace exploration to learn more about the trade-off surface. Thereby, the design space is reduced to the set of optimal trade-offs: a first step in selecting an appropriate implementation. However, generating the Pareto-optimal front can be computationally expensive and is often infeasible, because th
3、e complexity of the underlying application prevents exact methods from being applicable. Evolutionary algorithm (Eas) are an alternative: they usually do not guarantee to identify optimal trade-offs but try to find a good approximation, i. e, a set of solution that are (hopefully) not too far away f
4、rom the optimal front. Various multi-objective Eas are available, and certainly we are interested in the technique that provides the best approximation for a given problem.For this reason, comparative studies are conducted, they aim at revealing strengths and weaknesses of certain approaches and at
5、identifying the most promising algorithms. This, in turn, leads to the question of how to compare the performance of multi-objective optimizers. The notion of performance includes both the quality of the outcome as well as the computational resources needed to generate this outcome.Concerning the la
6、tter aspect, it is common practice to keep the number of fitness evaluations or the overall runtime constantin this sense, there is no difference between single and multi-objective optimization. As to the quality aspect, however, there is a difference. In single-objective optimization, we can define
7、 quality by means of the objective function: the smaller (or larger) the value, the better the solution. In contrast, it is not clear what quality means in the presence of several optimization criteria: closeness to the optimal front, coverage of a wide range of diverse solutions, or other propertie
8、s? Therefore, it is difficult to define appropriate quality measures for approximations of the Pareto-optimal front, and as a consequence.Hence, binary quality indicators represent the lowest level of representation on which it is still possible to detect whether an algorithm performs better than an
9、other in terms of the quality of the outcomes. On the other hand,this does not mean that unary quality indicators are generally useless. In conjunction with a compatible and complete comparison method,they can be used to further differentiate between incomparable approximation sets and to focus on s
10、pecific,usually problem-dependent aspects.However,we have to be aware that they often represent preference information and therefore for each problem the assumptions and knowledge exploited should be clearly specified.A more detailed discussion of this issue can be found in.(From:IEEE transactions o
11、n evolutionary computation, vol. 7, no. 2, April 2003)绩效考核对多目标管理的优化绩效考核作为人力资源管理的一项重要职能，其重要性和必要性已经越来越被很多企业所接受，很多的企业在建立绩效考核管理系统方面可谓不遗余力，还有很多企业聘请专业咨询机构协助建立或者优化绩效考核系统。然而，客观的说，不是每家企业都可以实现预期效果。于是有的企业就对绩效考核系统提出质疑，更有甚者干脆全盘否定和废气辛辛苦苦建立起来的绩效考核体系。不可否认，绩效考核是一项复杂度较高的管理技术，所以绩效考核体系本身是否合理是否科学会影响其实施效果。然而，完全将没有实现预期效果的
12、责任归咎于绩效考核体系本身是不恰当的。管理作为一门实践科学，没有任何一种管理方法或技术可以适用于任何环境，真正有效的管理技术往往不是最先进最科学的技术，而是与环境最匹配的技术。换言之，任何管理技术有效发挥作用都存在一定的管理假设和前提。绩效考核管理体系是整个企业管理体系中的一个子系统，绩效考核管理体系不可能也不应该游离于整个管理体系之外，这个子系统的运作必然会受到整个企业管理体系的影响。所以，有效的绩效考核体系也需要有与之相对应的管理假设。考察国内外企业的实施绩效考核的实践，为了实现绩效考核的预期效果，不同的企业都会有自己的“绝招”。有的是使用行为锚定评分法而有的是使用关键事件法：有的是每个月
13、进行考核而有的是每年进行考核，有的是由直接上级进行考核而有的却实行360度考核，可谓是“八仙过海，各显神通”。然而，真正有效的绩效考核体系都具备以下特征：管理不是目的而是手段，绩效考核也是如此。企业为什么要实施绩效考核？有人可能会说管理者为了防止职员“偷懒”，有人说可以通过绩效考核奖励优秀职员淘汰不称职职员这些固然没错，但如果把绩效考核的全部意义界定与此就未免太狭隘了。从管理控制理论上讲，绩效考核不仅是一项重要的人力资源管理职能，还是保证实现组织目标的有效管理控制手段，一方面组织通过绩效考核体系把组织目标分解成为个体绩效目标，同时通过绩效考核来衡量组织现状，并将现状和未来目标进行比较，寻找其中的差距，在此基础上调整和优化企业的资源（人力资源只是其中一项资源）和管理机制来不断地缩小差距，最后实现组织目标。所以说，实现组织目标才是绩效考核的真正出发点。绩效考核应该考什么？一种非常普遍的说法就是“德”“能”“勤”“绩”。然而果真如此吗？真的需要每年、每个季度甚至每个月对员工的品德和能力进行考核吗？一（摘自：IEEE进化计算的交易，2003年4月，第七卷，第二期）4