A genetic algorithm based on prepotency evolution using chaotic initiation used for network training

The concept of "prepotency" is introduced in evolution algorithm. The logistic mapping as a simple and powerful device in the chaos theory is combined with the newly proposed prepotency evolution (PE) algorithm to formulate a new genetic algorithm. PE with a population initialized by chaotic numbers is applied to the multiple-layer feed-forward ANN training (PECNN). The logistic mapping ensures the PE starts each time from different initial population never used before. The newly designed PE operator partially includes the crossover and mutation operations implicitly. The proposed algorithm has a higher convergence speed comparing to the conventional GA. During the PE operation the distances between members would become smaller and smaller until all members turning to be almost identical with the potential best minimum being found. It does not waste searching time surrounding the testing minima like the conventional GA and not show symptoms of overfitting to the training set samples. The combination of logistic mapping and PE used in ANN training makes PECNN be able to test lots of minima rapidly and effectively. This greatly enlarges the opportunity to find the global minimum. The proposed algorithm has been testified by prediction of the frequency data of tetrahedral vibration modes (nu(1) and nu(2)) of tetrahalide MX(4)(n) ions. The results obtained by the proposed PECNN compared favorably with those of the conventional chemometric method PLS regression.     

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。     

基于改进遗传算法的蛋白质三维折叠模拟

根据氨基酸的序列预测蛋白质的空间结构在基因治疗药物分子设计等方面有巨大的潜在应用价值.本研究基于HP格子模型利用改进的遗传算法预测了蛋白质的三维空间结构.改进的遗传算法引入了克隆体数量限制策略、巢穴竞争选择策略及局部优化策略等.实验结果表明,改进的遗传算法显著地提高了蛋白质结构的预测效率,模拟的蛋白质结构紧凑,更接近真实蛋白质的构型.     

Genetic training of network using chaos concept: application to QSAR studies of vibration modes of tetrahedral halides

The chaotic dynamical system is introduced in genetic algorithm to train ANN to formulate the CGANN algorithm. Logistic mapping as one of the most important chaotic dynamic mappings provides each new generation a high chance to hold GA's population diversity. This enhances the ability to overcome overfitting in training an ANN. The proposed CGANN has been used for QSAR studies to predict the tetrahedral modes (nu(1)(A1) and nu(2)(E)) of halides [MX(4)](epsilon). The frequencies predicted by QSAR were compared with those calculated by quantum chemistry methods including PM3, AM1, and MNDO/d. The possibility of improving the predictive ability of QSAR by including quantum chemistry parameters as feature variables has been investigated using tetrahedral tetrahalide examples.     

Strategies for increasing the efficiency of a genetic algorithm for the structural optimization of nanoalloy clusters

An improved genetic algorithm (GA) is described that has been developed to increase the efficiency of finding the global minimum energy isomers for nanoalloy clusters. The GA is optimized for the example Pt12Pd12, with specific investigation of: the effect of biasing the initial population by seeding; the effect of removing specified clusters from the population ("predation"); and the effect of varying the type of mutation operator applied. These changes are found to significantly enhance the efficiency of the GA, which is subsequently demonstrated by the application of the best strategy to a new cluster, namely Pt19Pd19.     

Development of a multipopulation parallel genetic algorithm for structure solution from powder diffraction data

Previously, the genetic algorithm (GA) approach for direct-space crystal structure solution from powder diffraction data has been applied successfully in the structure determination of a range of organic molecular materials. In this article, we present a further development of our approach, namely a multipopulation parallel GA (PGA), which is shown to give rise to increased speed, efficiency, and reliability of structure solution calculations, as well as providing new opportunities for further optimizing our GA methodology. The multipopulation PGA is based on the independent evolution of different subpopulations, with occasional interaction (e.g., transfer of structures) allowed to occur between the different subpopulations. Different strategies for carrying out this interpopulation communication are considered in this article, and comparisons are made to the conventional single-population GA. The increased power offered by the PGA approach creates the opportunity for structure determination of molecular crystals of increasing complexity.     

Massive docking of flexible ligands using environmental niches in parallelized genetic algorithms

Virtual screening of large libraries of small compounds requires fast and reliable automatic docking methods. In this article we present a parallel implementation of a genetic algorithm (GA) and the implementation of an enhanced genetic algorithm (EGA) with niching that lead to remarkable speedups compared to the original version AutoDock 3.0. The niching concept is introduced naturally by sharing genetic information between evolutions of subpopulations that run independently, each on one CPU. A unique set of additionally introduced search parameters that control this information flow has been obtained for drug‐like molecules based on the detailed study of three test cases of different complexity. The average docking time for one compound is of 8.6 s using eight R10,000 processors running at 200 MHz in an Origin 2000 computer. Different genetic algorithms with and without local search (LS) have been compared on an equal workload basis showing EGA/LS to be superior over all alternatives because it finds lower energy solutions faster and more often, particularly for high dimensionality problems. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1971–1982, 2001     

Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface

The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi-linear combinations of nonlinear functions, is indicated. The VP algorithm does not distinguish the weakly nonlinear parameters from the nonlinear ones and it does not apply to the model functions which are multi-linear combinations of nonlinear functions.     

近红外分析中光谱预处理及波长选择方法进展与应用

光谱预处理和波长选取方法在近红外光谱分析技术中相当重要。本文综述了常用的NIR预处理和波长选取方法及这一领域的最新进展,详细介绍正交信号校正(OSC)、净分析信号(NAS)和小波变换(WT)等新光谱预处理方法以及无信息变量消除(UVE)和遗传算法(GA)等波长选取方法,并给出了这些方法的具体算法和一些应用实例。     

Flexible ligand docking using a genetic algorithm

Summary Two computational techniques have been developed to explore the orientational and conformational space of a flexible ligand within an enzyme. Both methods use the Genetic Algorithm (GA) to generate conformationally flexible ligands in conjunction with algorithms from the DOCK suite of programs to characterize the receptor site. The methods are applied to three enzyme-ligand complexes: dihydrofolate reductase-methotrexate, thymidylate synthase-phenolpthalein and HIV protease-thioketal haloperidol. Conformations and orientations close to the crystallographically determined structures are obtained, as well as alternative structures with low energy. The potential for the GA method to screen a database of compounds is also examined. A collection of ligands is evaluated simultaneously, rather than docking the ligands individually into the enzyme.Abbreviations GA genetic algorithm; dhfr, dihydrofolate reductase - mtx methotrexate - ts thymidylate synthase - fen phenolphalein - HIV human immune deficiency virus - hivp HIV protease - thk thioketal haloperidol     

A genetic algorithm for flexible molecular overlay and pharmacophore elucidation

Summary A genetic algorithm (GA) has been developed for the superimposition of sets of flexible molecules. Molecules are represented by a chromosome that encodes angles of rotation about flexible bonds and mappings between hydrogen-bond donor proton, acceptor lone pair and ring centre features in pairs of molecules. The molecule with the smallest number of features in the data set is used as a template, onto which the remaining molecules are fitted with the objective of maximising structural equivalences. The fitness function of the GA is a weighted combination of: (i) the number and the similarity of the features that have been overlaid in this way; (ii) the volume integral of the overlay; and (iii) the van der Waals energy of the molecular conformations defined by the torsion angles encoded in the chromosomes. The algorithm has been applied to a number of pharmacophore elucidation problems, i.e., angiotensin II receptor antagonists, Leu-enkephalin and a hybrid morphine molecule, 5-HT_1D agonists, benzodiazepine receptor ligands, 5-HT₃ antagonists, dopamine D₂ antagonists, dopamine reuptake blockers and FKBP12 ligands. The resulting pharmacophores are generated rapidly and are in good agreement with those derived from alternative means.     

Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) studies based on chemometric techniques are reviewed. Partial least squares (PLS) is introduced as a novel robust method to replace classical methods such as multiple linear regression (MLR). Advantages of PLS compared to MLR are illustrated with typical applications. Genetic algorithm (GA) is a novel optimization technique which can be used as a search engine in variable selection. A novel hybrid approach comprising GA and PLS for variable selection developed in our group (GAPLS) is described. The more advanced method for comparative molecular field analysis (CoMFA) modeling called GA-based region selection (GARGS) is described as well. Applications of GAPLS and GARGS to QSAR and 3D-QSAR problems are shown with some representative examples. GA can be hybridized with nonlinear modeling methods such as artificial neural networks (ANN) for providing useful tools in chemometric and QSAR.     

Chemically driven variable selection by focused multimodal genetic algorithms in mid-IR spectra

Four genetic-algorithm-based approaches to variable selection in spectral data sets are presented. They range from a pure black-box approach to a chemically driven one. The latter uses a fitness function that takes into account not only typical parameters like the number of errors when classifying a training set but also the chemical interpretability of the selected variables. In order to cope with the fact that multiple solutions may be acceptable, a multimodal genetic algorithm (GA) is employed and the most satisfactory solution selected. The multimodal GA uses two populations (denominated "hybrid two populations" GA or HTP-GA): a classical population, from which potential solutions emerge, and a new population, which maintains diversity in the search space (as required by multimodal problems). Results show that the HTP-GA approach improves the chemical understanding of the selected solution (compared to other GA approaches) and that the classification capabilities of the approach are still good. All of the GA strategies for variable selection were compared with a classical parametric technique, Procrustes rotation, which does not consider interpretability.     

遗传算法与遗传编程的联用

遗传算法 ( Genetic algorithm)是一种概率搜索算法 [1] ,它的搜索过程与自然界生物进化过程相似[2 ] ,在搜索过程中不易陷入局部最优 .遗传编程 ( Genetic programming)是近年来随着程序设计自动化而产生和发展起来的一种自动编程技术 [3] .它继承了遗传算法的基本思想 ,是一种更广泛的适应系统方法 [4 ] .本文将遗传算法和遗传编程相结合 ,构造了既有广泛的搜索能力 ,又有很强的局部精化能力的 GA- GP算法 ,充分利用了遗传算法局部精化能力强、遗传编程适应面广的优点 .该算法应用范围广 ,可应用于多种非线性函数式及分式关系的建模 .1…     

Genetic algorithms: A robust scheme for geometry optimizations and global minimum structure problems

A Genetic Algorithm for Geometry Optimizations (GALGO) program has been developed to study the efficiency of this method of finding global minimum structures. Using a semiempirical tight-binding potential, the behavior of different genetic algorithm (GA) operators has been tested for the linear chain isomer of a C₈ cluster. An optimum set of parameters for the GA operators is proposed for this problem and afterward is used to obtain the global minimum structure of rare-gas atomic clusters of up to 13 atoms using the 12–6 Lennard-Jones interatomic pair potential. © 1995 by John Wiley & Sons, Inc.     

Partial Least Squares Modeling and Genetic Algorithm Optimization in Quantitative Structure-Activity Relationships

Abstract

Quantitative structure-activity relationship (QSAR) studies based on chemometric techniques are reviewed. Partial least squares (PLS) is introduced as a novel robust method to replace classical methods such as multiple linear regression (MLR). Advantages of PLS compared to MLR are illustrated with typical applications. Genetic algorithm (GA) is a novel optimization technique which can be used as a search engine in variable selection. A novel hybrid approach comprising GA and PLS for variable selection developed in our group (GAPLS) is described. The more advanced method for comparative molecular field analysis (CoMFA) modeling called GA-based region selection (GARGS) is described as well. Applications of GAPLS and GARGS to QSAR and 3D-QSAR problems are shown with some representative examples. GA can be hybridized with nonlinear modeling methods such as artificial neural networks (ANN) for providing useful tools in chemometric and QSAR.     

Evaluation of Lipase Production by Genetic Algorithm and Particle Swarm Optimization and Their Comparative Study

This paper presents the nature-inspired genetic algorithm (GA) and particle swarm optimization (PSO) approaches for optimization of fermentation conditions of lipase production for enhanced lipase activity. The central composite non-linear regression model of lipase production served as the optimization problem for PSO and GA approaches. The overall optimized fermentation conditions obtained thereby, when verified experimentally, have brought about a significant improvement (more than 15 U/gds (gram dry substrate)) in the lipase titer value. The performance of both optimization approaches in terms of computational time and convergence rate has been compared. The results show that the PSO approach (96.18 U/gds in 46 generations) has slightly better performance and possesses better convergence and computational efficiency than the GA approach (95.34 U/gds in 337 generations). Hence, the proposed PSO approach with the minimal parameter tuning is a viable tool for optimization of fermentation conditions of enzyme production.