EXPLORATION/EXPLOITATION TRADEOFF WITH CELL-SHIFT AND HEURISTIC CROSSOVER FOR EVOLUTIONARY ALGORITHMS

In order to tradeoff exploration/exploitation and inspired by cell genetic algorithm a cellshift crossover operator for evolutionary algorithm （EA） is proposed in this paper. The definition domain is divided into n-dimension cubic sub-domains （cell） and each individual locates at an ndimensional cube. Cell-shift crossover first exchanges the cell numbers of the crossover pair if they are in the different cells （exploration） and subsequently shift the first individual from its initial place to the other individual＇s cell place. If they are already in the same cell heuristic crossover （exploitation） is used. Cell-shift/heuristic crossover adaptively executes exploration/exploitation search with the vary of genetic diversity. The cell-shift EA has excellent performance in terms of efficiency and efficacy on ten usually used optimization benchmarks when comparing with the recent well-known FEP evolutionary algorithm.     

基于克隆思维进化算法的机器人足球协作策略

针对足球机器人比赛系统的实时性要求,采用了一种克隆思维进化算法对足球机器人比赛系统的高层策略系统进行优化。克隆思维进化算法集免疫机制与进化机制于一体,在发挥思维进化算法优势的基础上增加了克隆(复制)、克隆重组、克隆变异和克隆选择等算子,既保持了种群的多样性,又提高了算法的收敛速度。足球比赛场上的瞬时信息作为抗原,待选策略作为抗体,二者均采用二进制编码方式。用克隆思维进化算法对抗体群进行优化,实验结果表明,采用该算法能快速找到最佳策略,简化了足球机器人决策系统,提高了决策效率。     

Multi-objective carrier chaotic evolutionary algorithm for DNA sequences design

DNA computing is a new vista of computation, which is of biochemical type. Since each piece of information is encoded in biological sequences, their design is crucial for successful DNA computation. DNA sequence design is involved with a number of design criteria, which is difficult to be solved by the traditional optimization methods. In this paper, the multi-objective carrier chaotic evolution algorithm (MCCEA) is introduced to solve the DNA sequence design problem. By merging the chaotic search base on power function carrier, a set of good DNA sequences are generated. Furthermore, the simulation results show the efficiency of our method.     

P systems based multi-objective optimization algorithm

Based on P systems, this paper proposes a new multi-objective optimization algorithm (PMOA). Similar to P systems, PMOA has a cell-like structure. The structure is dynamic and its membranes merge and divide at different stages. The key rule of a membrane is the communication rule which is derived from P systems. Mutation rules are important for the algorithm, which has different ranges of mutation in different membranes. The cooperation of the two rules contributes to the diversity of the population, the conquest of the muhimodality of objective function and the convergence of algorithm. Moreover, the unique structure divides the whole population into several sub populations, which decreases the computational complexity. Almost a dozen popular algorithms are compared using several test problems. Simulation results illustrate that the PMOA has the best performance. Its solutions are closer to the true Pareto-optimal front     

Cell evolutionary algorithm: a new optimization method on ground-state energy of the atomic

The purpose of this paper is to present a new general approach to solve ground-state energies of the double-electron systems in a uniform magnetic field, in which the basic element of evolution is the set in the solution space, rather than the point. The paper defines the Cell Evolutionary Algorithm, which implements such a view of the evolution mechanism. First, the optimal set in which the optimal solution may be obtained. Then this approach applies the embedded search method to get the optimal solution. We tested this approach on the atomic structure, and the results show that it can improve not only the efficiency but also the accuracy of the calculations as it relates to this specific problem. Foundation item: Supported by the opening Foundation of state key Laboratory of Magnetic Resonance and Atomic and Molecular physics, Wuhan Institute of physics, Chinese Academy of Science (981510) Biography: Liu Lian-jun(1946-), female, Associate professor, research direction: atom and molecular physics.     

Two aspects of evolutionary algorithms

In this paper we discuss the paradigm of evolutionary algorithms (EAs). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult to solve. After introducing the main concepts of evolutionary algorithms, we concentrate on two issues: (1) self-adaptation of the parameters of EA, and (2) handling constraints. Biography: Zbigniew Michalewicz(1952-), male, Professor, research interest: evolutionary computation     

中小企业员工安全遵守行为演化路径

为研究中小企业员工安全遵守行为的演化规律,对中小企业不安全行为的原因进行了分析,并借助演化经济学方法,构建了中小企业员工与企业行为交往的支付矩阵。通过对交往过程的演化模型分析,揭示了个体策略选择对群体行为的影响。用数值仿真演示了群体不同初始比例和不同参数变化对演化趋势和结果的影响,研究发现:中小企业员工安全遵守行为演化系统收敛于两种模式,其中,(遵守,不检查)为理想模式,(违章,检查)为"不良锁定"模式;各参数的大小对系统收敛速度和收敛结果具有影响,企业在设计安全生产激励与约束机制时,通过设计科学合理的激励与约束参数,并通过调整参数的大小,可以引导员工的安全遵守行为,使中小企业员工安全行为向着预期方向和目标演进,达到制度优化的目的。     

证券投资者交易行为的进化博弈

应用进化博弈方法,建立了证券投资者交易行为演化的进化博弈模型,分析了在长时期内投资者交易行为的动态演化过程。结果表明,投资者交易行为的演化存在多重均衡的特征,且其演化的路径选择和均衡与投资者的效用函数和证券市场的初始环境密切相关,并受到投资者对风险资产收益的不合理预期、噪声交易风险、基本面风险、理性交易成本等诸多因素的影响。     

职业教育生态位构建与产业空间选择

职业教育生态位构建是主体通过改变自身生存策略,来完成对所在区位空间中有关的物质流和知识流的控制,是主体空间选择过程中的一种应激型进化方式和反馈作用。它从根本上打破了体系原有的平衡,为职业教育主体发展提供了动力。研究发现,生态位构建增强了职业教育空间选择效应,使职业教育分化为适应型、发展型和政策导向型三类。产业空间选择通过生态位构建作用为职业教育提供了不同的发展路径。     

基于演化聚类的社交媒体舆情分析方法综述

当前,社交媒体已经成为人们发布和获取信息的最主要渠道之一。由于大量用户的参与,社交媒体平台上已累计了海量的舆情数据,并且还在以指数级增加。如何发现和跟踪社交媒体中舆情变化是当前数据挖掘领域研究的热点问题之一。基于演化聚类的方法是分析社交媒体舆情的一类主要方法。主要从主题模型和矩阵/张量分解模型两方面来总结当前基于演化聚类的舆情分析方法,并且分析这两类方法的优缺点;最后对下一步研究进行了展望。