Neuro-genetic systems

The performance of neural networks (NN) can be enhanced by the use of genetic algorithms. There are two different approaches.  

This approach includes the use of GA in order to (a) generate the weights of a neural network (b) generate the architecture of a neural network and (c) generate both the weights and architecture of a neural network. 

This approach defines the evaluation function of the genetic algorithm as a neural network. The genetic operators are used to train the parameters of the neural network. The evaluation function is the forward stage of the neural network, while the genetic parameters are improved in the back-propagation stage of the neural network. 

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In many of the early applications of fuzzy logic, the s and B s in the if-then rules had to be calibrated by cut-and-trial to achieve a desired level of performance. During the past few years, however, the techniques related to the induction of rules from observations have been developed to a point where the calibration of rules—by induction from input-output pairs—can be automated in a wide variety of cases. Particularly effective in this regard are techniques centered on the use of neural network methods and genetic computing for purposes of system identification and optimization. Many of the so-called neuro-fuzzy and fuzzy-genetic systems are of this type. 

For nearly 80% of patients with epilepsy, the underlying etiology is unknown.8 The most common recognized causes of epilepsy are head trauma and stroke. Developmental and genetic defects are the cause of about 5% of cases of epilepsy. Central nervous system (CNS) tumors, central nervous system infections, and neurodegen-erative diseases are other common causes. Other important causes of epilepsy are human immunodeficiency virus infection or neuro-cysticercosis infection, primarily occurring in Latin America. 

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