以广义回归神经网络预测共同基金报酬

鉴于近年来许多相关文献成功地运用广义回归神经网络进行财经方面的预测,以及国内共同基金净值之预测与报酬率评估。通过搜集国内基金资料,以灰关联分析法进行各基金投资绩效分析,挑选投资绩效良好的共同基金作为投资标的;再以广义回归神经网络建立预测模型,与灰预测模型、多元回归模型进行预测能力及报酬率的比较分析。5种预测绩效评价指标、5组数据交互验证散布图及报酬率分析表明:广义回归神经网络在预测能力及预测报酬率上均有很好的表现。     

组播服务节点选择问题的求解算法

为了求解覆盖网络中最优组播服务节点MSN组合问题,提出一种基于混合实数编码的遗传算法(HRCGA)。采用实数编码方式对MSN组合进行编码,利用K-medoids聚类距离算子进行节点相似性度量,并根据MSN的实际特点,构造含有惩罚因子的适应度函数,限制不良个体参与进化。同时,针对标准遗传算法SGA局部搜索能力弱的特点,HRGCA引入个体进化控制策略。理论分析和仿真结果表明,该算法有效克服了传统K-medoids算法易陷入局部极小值、对初始中心选值敏感的问题。通过与传统SGA的仿真结果对比,HRCGA进一步提高了MSN组合全局寻优能力     

The Constrained Extremal Distribution Selection Method

Engineering design and policy formulation often involve the assessment of the likelihood of future events commonly expressed through a probability distribution. Determination of these distributions is based, when possible, on observational data. Unfortunately, these data are often incomplete, biased, and/or incorrect. These problems are exacerbated when policy formulation involves the risk of extreme events—situations of low likelihood and high consequences. Usually, observational data simply do not exist for such events. Therefore, determination of probabilities which characterize extreme events must utilize all available knowledge, be it subjective or observational, so as to most accurately reflect the likelihood of such events. Extending previous work on the statistics of extremes, the Constrained Extremal Distribution Selection Method is a methodology that assists in the selection of probability distributions that characterize the risk of extreme events using expert opinion to constrain the feasible values for parameters which explicitly define a distribution. An extremal distribution is then "fit" to observational data, conditional that the selection of parameters does not violate any constraints. Using a random search technique, genetic algorithms, parameters that minimize a measure of fit between a hypothesized distribution and observational data are estimated. The Constrained Extremal Distribution Selection Method is applied to a real world policy problem faced by the U.S. Environmental Protection Agency. Selected distributions characterize the likelihood of extreme, fatal hazardous material accidents in the United States. These distributions are used to characterize the risk of large scale accidents with numerous fatalities.     

A Novel Evolutionary Formulation of the Maximum Independent Set Problem

We introduce a novel evolutionary formulation of the problem of finding a maximum independent set of a graph. The new formulation is based on the relationship that exists between a graphs independence number and its acyclic orientations. It views such orientations as individuals and evolves them with the aid of evolutionary operators that are very heavily based on the structure of the graph and its acyclic orientations. The resulting heuristic has been tested on some of the Second DIMACS Implementation Challenge benchmark graphs, and has been found to be competitive when compared to several of the other heuristics that have also been tested on those graphs.     

弹性约束CSP及其基于遗传算法的交互式求解Agent

本文在回顾了约束满足问题(CSP)及其演进优化算法的基础上,提出了弹性约束CSP模型(ECSP),并将该模型形式化为六元组。ECSP模型是对已有的PCSP模型的改进。为了寻求ECSP问题的决策满意解,我们还设计了集成多Ageng、GA优化以及分布式并行计算技术的一种交互式多Ageng体系。我们详细阐述了其中的GA求解器算法,包括适应函数的确定、编码方式的选择、算子定义以及初始种群定义等。最后,我们用一个简单的算例证明了方法的有效性。     

基于遗传算法的经济性参数设计

针对田口方法的不足,本文在双因子方法DRA(dualresponseapproach)的基础上从经济的角度讨论了参数设计问题。考虑产品规格界限及质量损失的不对称性,建立了经济性参数设计模型,并利用遗传算法(GA)得到了最经济的过程均值与波动方差。最后给出一个实例验证了此模型是可行的,有效的。     

Non-standard methods in evolutionary computation

The paper presents non-standard methods in evolutionary computation and discusses their applicability to various optimization problems. These methods maintain populations of individuals with nonlinear chromosomal structure and use genetic operators enhanced by the problem specific knowledge.     

The coming revolution of biotechnology: A critique of Buttel

Frederick Buttel was one of the pioneers in studying the social impacts of biotechnology, claiming originally that it will involve profound changes in social structure. Recently Buttel turned around his argument proposing that, rather than revolutionary, biotechnology is more a substitutionist technological form to be applied to declining sectors of the economy than an epoch-making technology. This paper provides both external and internal critiques of Buttel's new position based on the concept of the third technological revolution, looking at the impact of new technologies as a global and interrelated phenomenon, and not on an individual case-by-case basis. The concluding section suggests the necessity of bringing into the analysis those living in the Third World: 60% of this population lives from agriculture and will be affected by the deployment of agricultural biotechnologies, whether through substitutionism or through totally new products.     

基于电商承诺送达机制的多目标低碳MDVRP优化研究

针对存在多配送站的电商物流配送问题,首先,考虑实际装载量对物流配送过程中车辆燃料消耗量的影响,建立燃料消耗量模型,并结合电商平台的承诺送达机制,构建配送延迟时间函数。随后,提出了以最小化物流成本和延迟收货时间的多目标多配送站车辆路径规划问题,建立该问题的混合整数规划模型。再次,采用基于分解的多目标遗传求解算法对问题进行求解。该算法采用矩阵编码的方式,设计了基于贪婪搜索策略的启发式初始化方法,考虑到贪婪搜索策略容易陷入局部最优的劣势,在算法迭代过程中,允许部分不可行解存在以扩大解空间的搜索范围,并进一步设计了遗传算法的交叉和变异算子。最后,以具体物流配送案例进行数值实验,实验结果表明所设计的算法对求解本文模型是有效的。     

基于遗传算法的具有主次仓库的企业资源供应链管理模型研究

本文研究如何在有容量限制的主次仓库间分配客户需求的企业资源供应链管理问题,当主仓库不能满足客户需求时,通过一个次仓库弥补主仓库的不足。首先把该问题构造成一个整数规划模型,其次,运用遗传算法加以解决,实际应用和数值分析说明了该遗传算法在解决这类问题中的有效性和实用生。