改进型遗传算法在加载天线设计中的运用

遗传算法是一种全新的优化搜索方法，可以用来解决各种复杂的实际问题。针对简单型遗传算法的一些不足之处，介绍了一种改进型的遗传算法，并阐述了其在短波宽带集总加载天线优化设计中的运用。     

Toward Fuzzy Optimization without Mathematical Ambiguity

Fuzzy programming has been discussed widely in literature and applied in such various disciplines as operations research, economic management, business administration, and engineering. The main purpose of this paper is to present a brief review on fuzzy programming models, and classify them into three broad classes: expected value model, chance-constrained programming and dependent-chance programming. In order to solve general fuzzy programming models, a hybrid intelligent algorithm is also documented. Finally, some related topics are discussed.     

基于遗传算法的优化排样

介绍了遗传算法以及二维不规则图形的计算机优化排样，研究了二维不规则图形排样中如何建立遗传算法的目标函数，如何实现交叉、变异运算等优化过程，找到了运用遗传算法实现二维不规则图形优化排样的方法。通过计算机编制了运用遗传算法进行二维不规则图形优化排样的运算程序，并对部分样形进行了优化排样，结果比较满意。     

达草灭对莱茵衣藻(Chlamydomonas reinhardtii) 品系遗传后效应的研究

通过将莱菌衣藻（Chlamydomonas reinhardtii）6种达草灭抗性突变株分别与野生型株、丧失合成叶绿素b能力的cbnI-43等基因突变株和精氨酸依赖型突变株杂交对其后代进行四分析与随机分析，发现在Nfr-4-Nfr-7突变株中达草灭抗性状只有单一核基因遗传的性质，而在Nfr-1、Nfr-3～Nfr-7抗性株的抗性性状都是由同一个nfr-1基因（norflurazon resisanse）的突变所决定的，而Nfr-4抗性株的抗性性状是由另一滚突变等位基因nfr-2所决定的。在Nfr-1和Nfr-3抗性株中除了nfr-1基因的突变还有nfr-3基因突变的参与。     

Robust handling of non‐linear constraints for GA optimization of aerodynamic shapes

A new approach to the robust handling of non‐linear constraints for GAs (genetic algorithms) optimization is proposed. A specific feature of the approach consists of the change in the conventional search strategy by employing search paths which pass through both feasible and infeasible points (contrary to the traditional approach where only feasible points may be included in a path). The method (driven by full Navier–Stokes computations) was applied to the problem of multiobjective optimization of aerodynamic shapes subject to various geometrical and aerodynamic constraints. The results demonstrated that the method retains high robustness of conventional GAs while keeping CFD computational volume to an acceptable level, which allowed the algorithm to be used in a demanding engineering environment. Copyright © 2004 John Wiley & Sons, Ltd.     

基于改进遗传算法的布局优化子问题

本针对子问题，构造了布局子问题(关于同构布局等价类)的改进遗传算法。将该算法应用于二维布局优化子问题，数值实验表明该算法能够在很好地保持图元的邻接关系的前提下找到子问题的最优解。由于布局优化问题可分解为有限个子问题，所以利用该算法可以找到整个布局优化问题的全局最优解。     

A New Algorithm for the Elimination of Common Subexpressions in Hardware Implementation of Digital Filters by Using Genetic Programming

A new algorithm based on Genetic Programming (GP) for the problem of optimization of Multiple constant Multiplication (MCM) by Common Subexpression Elimination(CSE) is developed. This method is used for hardware optimization for DSP systems. A solution based on Genetic Programming is shown in this paper. The performance of the technique is demonstrated in one- and multi-dimensional digital filters with constant coefficients.     

一种改进的共同进化遗传算法及其应用

将共同进化遗传算法应用于临床营养决策优化中,虽然取得了一定成果,但算法中仍存在早熟现象、局部搜索能量弱等问题,为此提出一种改进的共同进化模型。算法使用新的子群体划分方法和交叉算子,引入精英保留策略、信息交换原则和小生境技术。通过对糖尿病营养治疗的仿真表明,改进后的算法在保证群体多样性和避免早熟现象的前提下,提高了算法的局部和全局寻优能力,且膳食配方中营养素达标率符合要求,满足专家建议的糖尿病饮食需求。     

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.     

A floating-point genetic algorithm for solving the unit commitment problem

This paper proposes a floating-point genetic algorithm (FPGA) to solve the unit commitment problem (UCP). Based on the characteristics of typical load demand, a floating-point chromosome representation and an encoding–decoding scheme are designed to reduce the complexities in handling the minimum up/down time limits. Strategic parameters of the FPGA are characterized in detail, i.e., the evaluation function and its constraints, population size, operation styles of selection, crossover operation and probability, mutation operation and probability. A dynamic combination scheme of genetic operators is formulated to explore and exploit the FPGA in the non-convex solution space and multimodal objective function. Experiment results show that the FPGA is a more effective technique among the various styles of genetic algorithms, which can be applied to the practical scheduling tasks in utility power systems.