Genetic algorithms and neural networks

The search for optima within a given experimental space can also be realized by methodologies different from those that we have discussed before. We want to highlight two of them in this context, namely genetic algorithms and neural networks. 

Vivardli, F Giusti, G., Villani, M Campanini, R., Fariselli, P., Compiani, M. Casadio, R. (1995). LG ANN a parallel system combining a local genetic algorithm and neural networks for the prediction of secondary structure of proteins. ComputAppl Biosci 11,253-60. 

In addition to these fairly straightforward methods, several newer, more complex, analytical approaches have been developed. Among these are the multidimensional BCUT parameters developed by Pearlman or adaptations of the genetic algorithms and neural networks originally used for assessment of combinatorial library diversity. 

These limits can be pushed back by the extension of existing force fields and the development of new ones the refinement of generic force fields (see Section 3.3) quantum-mechanically driven molecular mechanics (e.g., for transition states see Section 3.3) the development of tools that refine parameter sets based on data banks, including genetic algorithms and neural networks or more conventional techniques (see Section 3.3 and 16.3). 

Hajela, P. (1997). Stochastic search in discrete structural optimization—Simulated annealing, genetic algorithms and neural networks. In W. Gutkowski (Ed.), Discrete Structural Optimization. CISM International Centre for Mechanical Sciences. Vol. 373. 

Elucidation of Organic Molecules from 13C NMR Spectra Using Genetic Algorithms and Neural Networks. 

Hunger J, Huttner G (1999) Optimization and analysis of force field parameters by combination of genetic algorithms and neural networks. J Comput Chem 20(4) 455-471... 

Meiler and M. Will. Automated structure elucidation of organic molecules from NMR spectra using genetic algorithms and neural networks./. Chem. Inf Comput. Sci., 41 1535-1546,2001. 

The high degree of complexity of typical real problems implies that the final method used to solve a problem is more often a combination of several methods, such as soft computing techniques (namely, fuzzy logic, genetic algorithms, and neural networks) with more classic ones (such as algebraic, analytical, numerical, and stochastic methods). 

Westphal, H. and Bornholdt, S. (1997) Lithofacies Prediction from Wireline Logs with Genetic Algorithms and Neural Networks, Z. dt. geol. Ges. 147 465-474. 

Schaffer, J.D., Whitley, D., and Eshelman, L.J. (1992) Combinations of Genetic Algorithms and Neural Networks A Survey of the State of the Art, in COGANN-92 International Workshop on Combinations of Genetic Algorithms and Neural Networks, Baltimore, MD (1992), IEEE Computer Society Press, Los Alamitos and references therein. 

A helpful starting point for further investigation is Learning Classifier Systems From Foundations to Applications.1 The literature in classifier systems is far thinner than that in genetic algorithms, artificial neural networks, and other methods discussed in this book. A productive way to uncover more... 

Poloni, C., Giurgevich, A., Onesti, L., and Pediroda, V. (2000). Hybridization of a multiobjective genetic algorithm, a neural network, and a classical optimizer for complex design problem in fluid dynamics. Comp. Methods App. Mech. and Eng., 186, (2-4), 403-420. 

A. Habibi-Yangjeh, E. Pourbasheer, and M. Danandeh-Jenagharad, Application of principal component-genetic algorithm-artificial neural network for prediction acidity constant of various nitrogen-containing compounds in water, Monatsh. Chem. 140 (2009), pp. 15-27. 

In a series of papers, personnel from Novartis and the University of Basel in Switzerland have highlighted the pros and cons of neural networks for immediate release tablets [37-40]. In other studies neural networks have been found useful in modeling tablet formulations of antacids [41], plant extracts [42], theophylline [43], and diltiazem [44]. In a recent paper Lindberg and Colbourn [45] have used neural networks, genetic algorithms, and neurofuzzy to successfully analyze historical data from three different immediate-release tablet formulations. 

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. 

Nature-inspired Methods in Chemometrics Genetic Algorithms and Artificial Neural Networks, edited by R. Leardi... 

Developments in computer technology promoted the use of computationally demanding methods such as artificial neural networks, genetic algorithms, and multiway data analysis. 

M. Bos and H. T. Weber, Comparison of the training of neural networks for quantitative X-ray fluorescence spectrometry by a genetic algorithm and backward error propagation. Anal. Chim. Acta, 247(1), 1991, 97-105. 

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