DNA序列比对分析中的统计特征方法

提出了一种新的基于统计的序列特征定义,并以此对进化树进行构建.同时对这一方法进行了分类测试和进化树构建测试,发现所得测试结果与同样是用进化树进行构建的基于两序列比对的传统算法比较,其性能和适用性都有明显改善.     

基于进化规划的自适应IIR滤波

针对基于梯度下降算法的自适应IIR滤波器（AIIRF）具有潜在的不稳定性和性能指标函数容易陷入局部极小而导致性能下降等问题，本文将进化规划用于AI－IRF的优化设计，不仅解决了AIIRF系统稳定性问题，而且有效地实现了滤波器性能指标函数的全局寻优和快速收敛，同时允许大动态范围的输入号。计算机仿真结果验证了基于进化规划算法的AIIRF的性能优于基于梯度算法的AIIRF，尤其对高阶、极点靠近单位圆的自适应IIR滤波器。     

不完全信息下的最终报价仲裁

基于争议双地仲裁人的裁决值有不同的概率估计，而这些估计是双方共同知识这一前提分析最终报价中仲裁，把它构造成一个不完全信息的非零和对策模型。然后给出了局部、全局Ｎａｓｈ平衡报价策略存在唯一的充分条件，最后探讨了双方认识存在的差异对争议人报价行为的影响。     

多约束的桥梁结构拓扑优化

基于ESO (Evolutionary Structural Optimization)的拓扑优化方法,本文提出了一种适应于桥梁结构的拓扑优化方法。引进了两种性能指标公式来确定最佳拓扑设计。另外,为了更有效地尝试改进最终设计的细节,而又不进行更精细有限元网格的完整分析,这里提出了一种精细网络设计方案。再者,结合细啃技术,完成了考虑应力、位移、频率约束的斜拉桥优化设计。通过几种桥梁的设计优化,表明该方法的可应用性、简洁性和有效性。     

Neural networks for contract bridge bidding

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.     

Minimum spanning trees made easier via multi-objective optimization

Many real-world problems are multi-objective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front, multi-objective optimization problems are considered as more difficult than single-objective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore, it is possible that a single-objective problem can be solved more efficiently via a generalized multi-objective model of the problem. That this is indeed the case is proved by investigating the computation of minimum spanning trees.This work was supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the Collaborative Research Center Computational Intelligence (SFB 531) and by the German-Israeli Foundation (GIF) in the project Robustness Aspects of Algorithms.     

一种新的基于极大似然估计的系统辨识方法

进化计算是一种基于自然选择和自然变异机制的并行随机优化算法。由于递推极大似然估计法难以实现性能指标函数的全局寻优，所以本文提出了将改进的进化计算用于系统参数辨识的方法。计算机仿真结果表明，该算法能够实现似然函数的全局寻优。     

粒子群优化算法及其应用

粒子群优化（PSO）算法是一种新颖的演化算法，它属于一类随机全局优化技术，PSO算法通过粒子间的相互作用在复杂搜索空间中发现最优区域。PSO的优势在于简单而又功能强大。介绍了基本的PSO算法、研究现状及其应用，并讨论将来可能的研究内容。     

Controlling the losing probability in a monotone game

We deal with a complex game between Alice and Bob where each contender’s probability of victory grows monotonically by unknown amounts with the resources employed. For a fixed effort on Alice’s part, Bob increases his resources on the basis of the results for each round (victory, tie or defeat) with the aim of reducing the probability of defeat to below a given threshold. We read this goal in terms of computing a confidence interval for the probability of losing and realize that the moves in some contests may bring in an indeterminacy trap: in certain games Bob cannot simultaneously have both a low probability-of-defeat measure and a narrow confidence interval. We use the inferential mechanism called twisting argument to compute the above interval on the basis of two joint statistics. Careful use of such statistics allows us to avoid indeterminacy.