矿井掘进工作面安全评价的灰色聚类方法

本文用灰色聚类方法[1]讨论了煤矿掘进工作面安全评价问题,提出了一种新的安全评价方法─灰色聚类综合法。同时,本文对灰色聚类的算法作了改进,改进后的算法便于记忆和应用。     

Genetic programming as a means for programming computers by natural selection

Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields. In genetic programming, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (sexual recombination) operator appropriate for genetically mating computer programs. Genetic programming is illustrated via an example of machine learning of the Boolean 11-multiplexer function and symbolic regression of the econometric exchange equation from noisy empirical data.Hierarchical automatic function definition enables genetic programming to define potentially useful functions automatically and dynamically during a run, much as a human programmer writing a complex computer program creates subroutines (procedures, functions) to perform groups of steps which must be performed with different instantiations of the dummy variables (formal parameters) in more than one place in the main program. Hierarchical automatic function definition is illustrated via the machine learning of the Boolean 11-parity function.     

A Genetic Algorithm Based Modification on the LTS Algorithm for Large Data Sets

The authors introduce an algorithm for estimating the least trimmed squares (LTS) parameters in large data sets. The algorithm performs a genetic algorithm search to form a basic subset that is unlikely to contain outliers. Rousseeuw and van Driessen (2006 Rousseeuw , P. J. , van Driessen , K. ( 2006 ). Computing LTS regression for large data sets . Data Mining and Knowledge Discovery 12 : 29 – 45 .[Crossref], [Web of Science ®] , [Google Scholar]) suggested drawing independent basic subsets and iterating C-steps many times to minimize LTS criterion. The authors 'algorithm constructs a genetic algorithm to form a basic subset and iterates C-steps to calculate the cost value of the LTS criterion. Genetic algorithms are successful methods for optimizing nonlinear objective functions but they are slower in many cases. The genetic algorithm configuration in the algorithm can be kept simple because a small number of observations are searched from the data. An R package is prepared to perform Monte Carlo simulations on the algorithm. Simulation results show that the performance of the algorithm is suitable for even large data sets because a small number of trials is always performed.     

求凸二次函数极值的一种递推算法

本文通过讨论凸二次函数的几个结论,给出了求凸二次函数极值的一种递推算法．     

The Independent Localisations of Interaction and Learning in the Repeated Prisoner's Dilemma

The results of a series of computer simulations demonstrate how the introduction of separate spatial dimensions for agent interaction and learning respectively affects the possibility of cooperation evolving in the repeated prisoner's dilemma played by populations of boundedly-rational agents. In particular, the localisation of learning promotes the emergence of cooperative behaviour, while the localisation of interaction has an ambiguous effect on it.     

复杂网络社区结构问题综述

本文从社区的定义,社区结构评价指标,社区发现算法几个方面对复杂网络社区结构问题进行了综述。对现有的几种对社区的定义进行了较详细的阐述,介绍了几种比较有代表性的评价指标,并对复杂网络社区发现算法进行了分类总结,最后对复杂网络社区结构的发展做出展望。     

Learning by Objectives for Adaptive Shop-Floor Scheduling*

Effective production scheduling requires consideration of the dynamics and unpredictability of the manufacturing environment. An automated learning scheme, utilizing genetic search, is proposed for adaptive control in typical decentralized factory-floor decision making. A high-level knowledge representation for modeling production environments is developed, with facilities for genetic learning within this scheme. A multiagent framework is used, with individual agents being responsible for the dispatch decision making at different workstations. Learning is with respect to stated objectives, and given the diversity of scheduling goals, the efficacy of the designed learning scheme is judged through its response under different objectives. The behavior of the genetic learning scheme is analyzed and simulation studies help compare how learning under different objectives impacts certain aggregate measures of system performance.     

Carnivalesque activism as a public relations genre: A case study of the New Zealand group Mothers Against Genetic Engineering

This paper argues that carnivalesque protest comprises a particular genre of activist public relations which articulates conflict with, and resistance to, dominant discourses on controversial issues. A case study of the New Zealand group Mothers Against Genetic Engineering illustrates how the carnivalesque, by promoting community relationships and influencing public opinion through values of affect, can open up new spaces for public dialogue about social issues. It is argued that carnivalesque protest challenges the notion that collaboration should be a core professional value of public relations practice.     

长株潭城市群水资源承载力的实证研究

在界定水资源承载力概念的基础上,构建了长株潭城市群水资源承载能力的指标体系,采用基于遗传算法的投影寻踪聚类模型对长株潭城市群水资源承载能力进行了实证分析,提出了提高长株潭城市群水资源承载能力水平的几点应对措施。     

A hybrid genetic algorithm for train sequencing in the Korean railway

This article addresses the train-sequencing problem encountered in the Korean railway. It first presents a mixed integer programming model for the problem, in which the mileage must be balanced for each train route, while various field constraints must be satisfied, including overnight stay capacity and maintenance allocation restrictions. Then, it proposes a hybrid genetic algorithm as a solution approach to the problem. The proposed algorithm utilizes a modified elite group technique along with two heuristic procedures based on the mixed integer programming model. Finally, the proposed solution approach is tested with real-world data from the Korean railway. Numerical experiments under different conditions indicate that the proposed solution approach to the train-sequencing problem is promising.