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In this work MagnetoEncephaloGram (MEG) recordings of epileptic patients are modeled using a genetic programming approach. This is the first time that genetic programming is used to model MEG signal. Numerous experiments were conducted giving highly successful results. It is demonstrated that genetic programming can produce very simple nonlinear models that fit with great accuracy the observed data of MEG.

This study proposes an Artificial Neural Network (ANN) and Genetic Algorithm model for diagnostic risk factors selection in medicine. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Networks (PNNs) were used to face a medical disease prediction. Genetic Algorithm (GA) was used for pruning the PNN. The implemented GA searched for optimal subset of factors that fed the PNN to minimize the number of neurons in the ANN input layer and the Mean Square Error (MSE) of the trained ANN at the testing phase. Moreover, the available data was processed with Receiver Operating Characteristic (ROC) analysis to assess the contribution of each factor to medical diagnosis prediction. The obtained results of the proposed model are in accordance with the ROC analysis, so a number of diagnostic factors in patient's record can be omitted, without any loss in clinical assessment validity.

In recent years, there has been an increasing number of papers in the literature that use neural networks for financial time-series prediction. This paper focuses on the Greek Foreign Exchange Rate Currency Market. Using four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DM), the French Franc (FF) and the British Pound (BP), against the Greek Drachma, on a daily basis for a period of 11 years, we try to forecast the above time-series, using two different approaches. The first one involves RBF training using a Kalman filter, while the second one uses genetic algorithms in order to optimize the RBF network parameters. The goal of this effort, is, first, to predict, as accurately as possible, currencies future behaviour, in a daily predicting horizon and second, to have a comparison between the two methods.