Genetic algorithms prediction

In control situations, equation (9) is reduced to just one discrete point, controlled variable. In this case the objective function set the perturbation, as well as the non-controlled variables, and the genetic algorithm predictive control is performed. Table 4 presents a series of simulations, for some common perturbations, and presents the new calculated values for the manipulated variable. The references values used for these calculations are t those presented prior to the steady state optimisation. The controlled variable is the temperature, while the manipulated is the fuel. 

S. Sun, Reduced representation model of protein structure prediction statistical potential and genetic algorithms. Protein Sci. 2 (1993), 762-785. 

Talafous J, Sayre LM, Mieyal JJ, Klopman G. META. 2. A dictionary model of mammalian xenobiotic metabolism. J Chem Inf Comput Sci 1994 34 1326-33. Klopman G, Tu M, Talafous J. META. 3. A genetic algorithm for metabolic transform priorities optimization. J Chem Inf Comput Sci 1997 37 329-34. Langowski J, Long A. Computer systems for the prediction of xenobiotic metabolism. Adv Drug Del Rev 2002 54 407-15. 

Agatonovic-Kustrin S, Alany RG. Role of genetic algorithms and artificial neural networks in predicting phase behaviour of colloidal delivery systems. Pharm Res 2001 18 1049-55. 

Unger R (2004) The Genetic Algorithm Approach to Protein Structure Prediction 110 153-175... 

The prediction of stable structures that can be formed by groups of a few dozen atoms is computationally expensive because of the time required to determine the energy of each structure quantum mechanically, but such studies are increasingly valuable because of the need in nanochemistry to understand the properties of these small structures. The genetic algorithm is now widely used to help predict the stability of small atomic clusters.2... 

Abraham, N.L. and Probert, N.J., A periodic genetic algorithm with real space representation for crystal structure and polymorph prediction, Phys. Rev. B., 73, 224106,2006. 

Friese, T. P. Ulbig and S. Schulz. Use of Evolutionary Algorithms for the Calculation of Group Contribution Parameters in Order to Predict Thermodynamic Properties. Part 1 Genetic Algorithms. Comput Chem Eng 22 1559-1572 (1998). 

Cartwright, Sztandera and Chu50 have also used the combination of a neural network with a GA to study polymers, using the neural network to infer relationships between the structure of a polymer and polymer properties and the genetic algorithm to predict new promising polymer structures whose properties can be predicted by the network. 

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... 

Wegner, J.K. and Zell, A. Prediction of aqueous solubility and partition coefEcient optimized by a genetic algorithm based descriptor selection method. /. Chem. Inf. Comput. Sci. 2003, 43, 1077-1084. 

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... 

The program is capable of predicting the metabolites that could be generated from a novel xenobiotic. A genetic algorithm has been used to build and... 

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