基于改进排序遗传算法的径向基函数神经网络色谱峰解析

构造了以塔板模型为基函数的径向函数神经网络（P－RBFNN），为了使P－RBFNN具有结构重组能力，又在网络学习算法中引入鲁棒（Rubust)和随机全局最优的两阶段排序遗传算法：结构学习和进化。P－RBFNN结合改进的排序遗传算法很适合组分数未知的色谱（含重叠）峰解析。     

遗传算法在计算机辅助药物分子设计中的应用

作为一种重要的启发式优化算法,遗传算法在计算机辅助药物分子设计中得到了广泛的应用.本文介绍了遗传算法的基本概念以及工作原理,同时结合作者科研组的工作,就遗传算法在定量构效关系、构象分析、药效团模拟、分子对接以及虚拟组合化学等方面的应用做了系统的阐述。     

铅和富里酸化学形态模拟计算方法的比较

在阳极溶出伏安法获限Ｐｂ和富里酸的溶出电流和电位偏移数值的基础上采用一种新的全局优化方法－遗传算法模拟计算了水体中铅和富里酸的化学形态，对３种计算络合常数的方法进行了比较，误差分析的结果表明：对所研究的体系，电流迭代－遗传算法比电位偏移－遗传算法获得的结果更可靠。     

分析化学中非线性多元函数拟合的遗传算法

本文提出分析化学中非线性多元函数拟合的遗传算法，并以一组试验数据为对象，尝试了遗传算法的效果。结果表明，遗传算法性能良好，可望成为分析化学中各类非线性函数拟合，曲线校正的有效方法。     

遗传算法在分析化学中的应用

遗传算法是基于自然界生物进化基本法进而发展起来的一类新算法，在优化过程中，它无需体系的选验知识，能在许多局部较优中找到全局最优点，是一种全局最优化方法，能有效地处理复杂的非线性问题，有广阔的发展前景，目前在分析化学领域已经有多方面应用，本文简要地介绍遗传算法的原理及其在分析化学等方面的若干应用。     

二维泡沫稳定性与拓扑学性质关系的研究

研究了二维泡沫形成、进化及其拓扑学性质随时间的变化关系以及影响其稳定性的因素。探索了二维泡沫初始有序化的气泡进化和完全无序化的气泡进化的两种过程，并分析在无序化气泡的进化过程中，平均气泡面积随时间的变化呈现出α=1．5的幂指数关系，以及该指数大于Von Neuman定律的时间常数的可能原因。讨论了气泡的边数分布及其二阶矩。     

遗传算法及其在分析化学中的应用

遗传算法是模拟生物群体遗传滨基本原理解决问题的一种高效优化方法。本文综述了遗传算法的基本原理、基本过程、发展现状及其在分析化学中的应用。     

定量结构-活性/性质相关性研究中变量选择方法的研究——遗传算法和几种传统算法的比较

鉴于变量选择在QSAR／QSPR研究中的重要性。比较了遗传算法和几种传统的方法,如前进法、后退法及逐步回归法。结果表明,对于研究中所用数据,遗传算法较几种传统的方法为好,其原因可能由于传统的方法陷入了局部最优。遗传算法在变量较多的情况下方可显示出效率高和得到较好结果的优越性。对于变量的选择,遗传算法是一值得推荐的有效的方法。     

定量结构-活性/性质相关性研究中变量选择方法的研究——遗传算法和几种传统算法的比较

鉴于变量选择在 QSAR/QSPR研究中的重要性 ,比较了遗传算法和几种传统的方法 ,如前进法、后退法及逐步回归法 .结果表明 ,对于研究中所用数据 ,遗传算法较几种传统的方法为好 ,其原因可能由于传统的方法陷入了局部最优 .遗传算法在变量较多的情况下方可显示出效率高和得到较好结果的优越性 .对于变量的选择 ,遗传算法是一值得推荐的有效的方法     

定量结构-活性/性质相关性研究中变量选择方法的研究

鉴于变量选择在QSAR/QSPR研究中的重要性,比较了遗传算法和几种传统的方法,如前进法、后退法及逐步回归法.结果表明,对于研究中所用数据,遗传算法较几种传统的方法为好,其原因可能由于传统的方法陷入了局部最优.遗传算法在变量较多的情况下方可显示出效率高和得到较好结果的优越性.对于变量的选择,遗传算法是一值得推荐的有效的方法.     

A genetic algorithm for structure-based de novo design

Genetic algorithms have properties which make them attractive in de novo drug design. Like other de novo design programs, genetic algorithms require a method to reduce the enormous search space of possible compounds. Most often this is done using information from known ligands. We have developed the ADAPT program, a genetic algorithm which uses molecular interactions evaluated with docking calculations as a fitness function to reduce the search space. ADAPT does not require information about known ligands. The program takes an initial set of compounds and iteratively builds new compounds based on the fitness scores of the previous set of compounds. We describe the particulars of the ADAPT algorithm and its application to three well-studied target systems. We also show that the strategies of enhanced local sampling and re-introducing diversity to the compound population during the design cycle provide better results than conventional genetic algorithm protocols.     

遗传-因子分析光度法用于地质样品中15个稀土元素同时测定的研究

遗传因子分析方法（GFA）综合了遗传算法（GA）和迭代目标因子分析法（FA）的优点，不仅实现了校准模型的动态化，而且解决了多组分同时测定时收敛滞缓的问题。遗传-因子分析方法应用于15种稀土的同时测定，提高了分析结果的准确度。     

On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.     

Comparison of commercially available genetic algorithms: GAs as variable selection tool

Many commercially available software programs claim similar efficiency and accuracy as variable selection tools. Genetic algorithms are commonly used variable selection methods where most relevant variables can be differentiated from ‘less important’ variables using evolutionary computing techniques. However, different vendors offer several algorithms, and the puzzling question is: which one is the appropriate method of choice? In this study, several genetic algorithm tools (e.g. GFA from Cerius2, QuaSAR-Evolution from MOE and Partek’s genetic algorithm) were compared. Stepwise multiple linear regression models were generated using the most relevant variables identified by the above genetic algorithms. This procedure led to the successful generation of Quantitative Structure–activity Relationship (QSAR) models for (a) proprietary datasets and (b) the Selwood dataset.     

Computational and experimental approaches for investigating membranes diffusion behavior in model diesel fuel

Genetic algorithms trained support vector regression predicting model is conducted to research diffusion behavior of methylnaphthalene and dibenzothiophene in four different membranes of polymethyl methacrylate, polymethyl acrylate, polyvinyl chloride and polyvinyl alcohol in model diesel fuel. It is found that the polyvinyl chloride is optimal membrane material for improving the diffusion selectivity of methylnaphthalene and dibenzothiophene, which demonstrates that the polyvinyl chloride membrane is favorable to the diesel fuel desulfurization. Also, molecular dynamic simulation is applied to validating the performance of genetic algorithm trained support vector regression model. The results of genetic algorithm trained support vector regression model reveal that the simulation values are well agreed with the experimental data and molecular dynamic simulation results. Meanwhile, the performance of the genetic algorithms trained support vector regression predict model is better than that of the genetic algorithms trained neural network model, which indicates that genetic algorithms trained support vector regression method offers a new prospected decision-theoretic approach to the diesel desulfurization.     

基于遗传算法的神经网络为苯乙酰胺类农药构效关系建模的研究

探讨用遗传算法训练神经网络,为苯乙酰胺类化合物的QSAR建模,效果良好,神经网络可以反映复杂的构效关系,而引入遗传算法又有助于多层前传网在训练过程中跳出局部最小点,使收敛速度大大提高,并在预报精度上有显著改善.     

遗传算法与药物分子设计

本文对遗传算法及其近年来在药物设计中的应用进行了较为系统的介绍。遗传算法非常适合解决组合优化问题, 它在柔性分子构象搜寻、药效基团推测、蛋白质结构预测、分子对接、全新药物设计以及组合合成中都具有很大的应用潜力。     

Multiobjective Optimization of Industrial Petroleum Processing Units Using Genetic Algorithms

For many years most of refining processes were optimized using single objective approach, but practically such complex processes must be optimized with several objectives. Multiobjective optimization allows taking all of desired objectives directly and provide search of optimal solution with respect to all of them. Genetic algorithms proved themselves as a powerful and robust tool for multi-objective optimization. In this article, the review for a last decade of multi-objective optimization cases is provided. Most popular genetic algorithms and techniques are mentioned. From a practical point it is shown which objectives are usually chosen for optimization, what constraint and limitations might impose multi-objective optimization problem formulation. Different types of petroleum refining processes are considered such as catalytic and thermal.     

Optimisation methodologies and algorithms for research on catalysis employing high-throughput methods: comparison using the Selox benchmark

The Selox is a catalytic benchmark for the selective CO oxidation reaction in the presence of H(2), in the form of mathematical equations obtained via modelling of experimental results. The optimisation efficiencies of several Global Optimisation algorithms were studied using the Selox benchmark. Genetic Algorithms, Evolutionary Strategies, Simulated Annealing, Taboo Search and Genetic Algorithms hybridised with Knowledge Discovery procedures were the methods compared. A Design of Experiments search strategy was also exemplified using this benchmark. The main differences regarding the applicability of DoE and Global optimisation techniques are highlighted. Evolutionary strategies, Genetic algorithms, using the sharing procedure, and the Hybrid Genetic algorithms proved to be the most successful in the benchmark optimisation.     

A genetic algorithm for the automated generation of small organic molecules: Drug design using an evolutionary algorithm

Rational drug design involves finding solutions to large combinatorial problems for which an exhaustive search is impractical. Genetic algorithms provide a novel tool for the investigation of such problems. These are a class of algorithms that mimic some of the major characteristics of Darwinian evolution. LEA has been designed in order to conceive novel small organic molecules which satisfy quantitative structure-activity relationship based rules (fitness). The fitness consists of a sum of constraints that are range properties. The algorithm takes an initial set of fragments and iteratively improves them by means of crossover and mutation operators that are related to those involved in Darwinian evolution. The basis of the algorithm, its implementation and parameterization, are described together with an application in de novo molecular design of new retinoids. The results may be promising for chemical synthesis and show that this tool may find extensive applications in de novo drug design projects.