Genetic Algorithms GAs

GA is a programming technique that mimics biological evolution to find true or approximate solutions to optimization and search problems. They are a specific instance of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. GAs were developed by John Holland and his team [67]. 

GAs are optimization techniques that search a set of alternative solutions to find the best one. Solutions are represented in a vector, which is usually called a chromosome in the style of biological evolution. The basic constituents of the GA are as follows  

The resulting chromosomes should represent an optimum solution based on the fitness function and the termination criterion. 

Another application of GAs was published by Aires de Sousa et al. they used genetic algorithms to select the appropriate descriptors for representing structure-chemical shift correlations in the computer [69]. Each chromosome was represented by a subset of 486 potentially useful descriptors for predicting H-NMR chemical shifts. The task of a fitness function was performed by a CPG neural network that used the subset of descriptors encoded in the chromosome for predicting chemical shifts. Each proton of a compound is presented to the neural network as a set of descriptors obtaining a chemical shift as output. The fitness function was the RMS error for the chemical shifts obtained from the neural network and was verified with a cross-validation data set. 

3D Molecular Descriptors are molecule representations based on Cartesian coordinates and Euclidean distances. 

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Other methods which are applied to conformational analysis and to generating multiple conformations and which can be regarded as random or stochastic techniques, since they explore the conformational space in a non-deterministic fashion, arc genetic algorithms (GA) [137, 1381 simulation methods, such as molecular dynamics (MD) and Monte Carlo (MC) simulations 1139], as well as simulated annealing [140], All of those approaches and their application to generate ensembles of conformations arc discussed in Chapter II, Section 7.2 in the Handbook. 

Kohonen network Conceptual clustering Principal Component Analysis (PCA) Decision trees Partial Least Squares (PLS) Multiple Linear Regression (MLR) Counter-propagation networks Back-propagation networks Genetic algorithms (GA)... 

Models developed with selected subsets of descriptors, instead of all possible descriptors, can be more accurate and robust. In order to select adequate descriptors for each of the four classes of protons, genetic algorithms (GA) were used, and the results were compared with those obtained when all the descriptors were used. 

HTS data as well as virtual screening can guide and direct the design of combinatorial libraries. A genetic algorithm (GA) can be applied to the generation of combinatorial libraries [18. The number of compounds accessible by combinatorial synthesis often exceeds the number of compounds which can be syiithcsii ed... 

To determine the optimal parameters, traditional methods, such as conjugate gradient and simplex are often not adequate, because they tend to get trapped in local minima. To overcome this difficulty, higher-order methods, such as the genetic algorithm (GA) can be employed [31,32]. The GA is a general purpose functional minimization procedure that requires as input an evaluation, or test function to express how well a particular laser pulse achieves the target. Tests have shown that several thousand evaluations of the test function may be required to determine the parameters of the optimal fields [17]. This presents no difficulty in the simple, pure-state model discussed above. 

Full Width at Half Maximum Genetic Algorithm Gas Chromatography... 

The steps that a genetic algorithm (GA) takes in evolving the solution to a problem are shown in schematic form in Figure 5.3. 

The surface defined by an arbitrary fitness function, across which the genetic algorithm (GA) searches for a maximum or minimum. The surface may be complex and contain numerous minima and maxima. 

Genetic Algorithms (GA) are used in case of large combinatorial problems and can be applied e g. for example in complex value chain network design decisions (Chan/Chung 2004)... 

We may suppose that not all 600 wavelengths are useful for the prediction of nitrogen contents. A variable selection method called genetic algorithm (GA, Section 4.5.6) has been applied resulting in a subset with only five variables (wavelengths). Figure 1.3c and d shows that models with these five variables are better than models... 

Each of these two QSAR model searches led to pools of several thousands of statistically valid linear equations, expressing the estimate of the Cox2 pICso value as linear combinations of molecular descriptors selected by a Genetic Algorithm (GA) [57,... 

One such algorithm, the genetic algorithm (GA) has seen considerable usage in chemometrics PAT applications for variable selection [99-101]. GA operates in the following manner. 

Electronic descriptors were calculated for the ab initio optimized (RHG/STO-3G) structures. In addition, logP as a measure of hydrophobicity and different topological indices were also calculated as additional descriptors. A nonlinear model was constructed using ANN with back propagation. Genetic algorithm (GA) was used as a feature selection method. The best ANN model was utilized to predict the log BB of 23 external molecules. The RMSE of the test set was only... 

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