建筑工程投标报价优选模型

通过建立区间二次规划数学模型、灰色模型、多目标规划等模型，建立材料价格和用量的选择模型，提供投标单位一种便捷的调整工程造价和控制造价的手段。     

深亚微米条件下的多层VLSI／MCM层分配的遗传算法

本文提出了一个深亚微米条件下的多层ＶＬＳＭＣＭ有约束分层层分配的遗传算法。该算法分为两步：首先进行超层分配，使各线网满足Ｃｒｏｓｓｔａｌｋ约束，且超层数目最少；然后进行各超层的通孔最少化二分层。与目前的层分配算法相比，该遗传算法具有目标全面，全局优化能力强等特点，是一种可应用于深亚微米条件下的ＩＣ ＣＡＤ的有效分层方法。     

一种遗传-梯度混合算法

本文提出一种函数全局优化的遗传-梯度混合算法。该算法由选择、交叉和诱导变异算子组成。通过对自适应滤波和前向神经网络权值训练两个典型问题的数字仿真，表明该算法是一种快速有效的全局优化算法。     

A novel dynamic survivable routing in WDM optical networks with/without sparse wavelength conversion

In this paper, we study the dynamic survivable routing problem, both in optical networks without wavelength conversion and in optical networks with sparse wavelength conversion, and propose a novel hybrid algorithm for it based on the combination of mobile agents technique and genetic algorithms (GA). By keeping a suitable number of mobile agents in the network to cooperatively explore the network states and continuously report cycles (that are formed by two disjoint-link routes) into the routing tables, our new hybrid algorithm can promptly determine the first population of cycles for a new request based on the routing table of its source node, without the time consuming process associated with current GA-based lightpath protection schemes. We further improve the performance of our algorithm by introducing a more advanced fitness function that is suitable for both the above networks. Extensive simulation studies on the ns-2 network simulator show that our hybrid algorithm achieves a significantly lower blocking probability than the conventional survivable routing algorithms for all the cases we studied.     

Temperature effect on deposition rate of silicon nitride films

Temperature effects on deposition rate of silicon nitride films were characterized by building a neural network prediction model. The silicon nitride films were deposited by using a plasma enhanced chemical vapor deposition system and process parameter effects were systematically characterized by 2⁶⁻¹ fractional factorial experiment. The process parameters involved include a radio frequency power, pressure, temperature, SiH₄, N₂, and NH₃ flow rates. The prediction performance of generalized regression neural network was drastically improved by optimizing multi-valued training factors using a genetic algorithm. Several 3D plots were generated to investigate parameter effects at various temperatures. Predicted variations were experimentally validated. The temperature effect on the deposition rate was a complex function of parameters but N₂ flow rate. Larger decreases in the deposition rate with the temperature were only noticed at lower SiH₄ (or higher NH₃) flow rates. Typical effects of SiH₄ or NH₃ flow rate were only observed at higher or lower temperatures. A comparison with the refractive index model facilitated a selective choice of either SiH₄ or NH₃ for process optimization.     

SASEGASA: A New Generic Parallel Evolutionary Algorithm for Achieving Highest Quality Results

This paper presents a new generic Evolutionary Algorithm (EA) for retarding the unwanted effects of premature convergence. This is accomplished by a combination of interacting generic methods. These generalizations of a Genetic Algorithm (GA) are inspired by population genetics and take advantage of the interactions between genetic drift and migration. In this regard a new selection scheme is introduced, which is designed to directedly control genetic drift within the population by advantageous self-adaptive selection pressure steering. Additionally this new selection model enables a quite intuitive heuristics to detect premature convergence. Based upon this newly postulated basic principle the new selection mechanism is combined with the already proposed Segregative Genetic Algorithm (SEGA), an advanced Genetic Algorithm (GA) that introduces parallelism mainly to improve global solution quality. As a whole, a new generic evolutionary algorithm (SASEGASA) is introduced. The performance of the algorithm is evaluated on a set of characteristic benchmark problems. Computational results show that the new method is capable of producing highest quality solutions without any problem-specific additions.     

COPART:一种面向约束条件的自适应软硬件划分算法

本文介绍一种将自适应算法、遗传算法和列表调度算法结合起来应用于软硬件划分问题的算法COPARTART，并提出了一种基于约束条件的开销系数自适应调整方法，该方法所获得的划分结果能够良好体现设计目标。     

稀疏阵列天线综合的遗传算法优化

通过遗传算法优化选择常规满阵中一定比例的工作单元,使阵列在欠饱和状态下达到窄波束低副瓣的要求。以单元的工作状态和激励幅度为优化参量,波束宽度和副瓣电平为适值函数,通过适值缩放和多种群进化竞争使遗传进化稳定地收敛到全局最优。详细分析了稀疏阵的单元布局、稀疏率及单元激励对优化阵列特性的影响。     

POWER OPTIMIZATION OF FINITE STATE MACHINE BASED ON GENETIC ALGORITHM

Using state assignment to minimize power dissipation and area for finite state machines is computationally hard.Most of published results show that the reduction of switching activity often trades with area penalty.In this paper,a new approach is proposed.Experimental results show a significant reduction of switching activity without area penalty compared with previous publications.     

Fitting the control parameters of a genetic algorithm: An application to technical trading systems design

This paper studies the problem of how changes in the design of the genetic algorithm (GA) have an effect on the results obtained in real-life applications. In this study, focused on the application of a GA to the tuning of technical trading rules in the context of financial markets, our tentative thesis is that the GA is robust with respect to design changes. The optimization of technical trading systems is a suitable area for the application of the GA metaheuristic, as the complexity of the problem grows exponentially as new technical rules are added to the system and as the answer time is crucial when applying the system to real-time data. Up to now, most of GAs applications to this subject obviated the question of possible “design dependence” in their results. The data we report, based on our experiments, do not allow us to refute the hypothesis of robustness of the GA to design implementation, when applying to technical trading systems tuning.