Neural networks genetic function

B. Carse and T.C. Fogarty, Fast evolutionary learning of minimal radial basis function neural networks using a genetic algorithm. Lecture Notes in Computer Science, 1143, (1996) 1-22. 

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. 

Rost, B. and Sander, C. (1994) Combining Evolutionary Information and Neural Networks to Predict Protein Secondary Structure, PROTEINS Structure, Function, and Genetics 19 55-72. 

Other recently published correlative methods for predicting Tg include the group interaction modeling (GIM) approach of Porter (42), neural networks (43-45), genetic function algorithms (46), the CODESSA (acronym for Comprehensive Descriptors for Structural and Statistical Analysis ) method (47), the energy, volume, mass (EVM) approach (48,49), correlation to the results of semiempirical quantum mechanical calculations of the electronic structure of the monomer (50), and a method that combines a thermodynamic equation-of-state based on lattice fluid theory with group contributions (51). 

Also in chemistry artificial neural networks have found wide use. They have been used to fit spectroscopic data, to investigate quantitative structure-activity relationships (QSAR), to predict deposition rates in chemical vapor deposition, to predict binding sites of biomolecules, to derive pair potentials from diffraction data on liquids, " to solve the Schrodinger equation for simple model potentials like the harmonic oscillator, to estimate the fitness function in genetic algorithm optimizations, in experimental data analysis, to predict the secondary structure of proteins, to predict atomic energy levels, " and to solve classification problems from clinical chemistry, in particular the differentiation between diseases on the basis of characteristic laboratory data. ... 

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