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The already introduced generic Data Mining system SHOCID (System applying High Order Computational Intelligence in Data Mining) (Neukart et al., 2011) applies Computational Intelligence (CI) paradigms for solving any numeric Data Mining problem statement. Within this paper, we introduce the evolutionary approach by which the system is able to decide on its own, which of the possible evolutionary approaches suits best for solving a presented problem statement. Moreover, the system is, by the application of genetic algorithms, able to adapt the architecture and learning method of the Data Mining solution until coming to or at least close to the optimal solution.

Artificial ImmuneSystem (AIS)-inspired NeuroEvolution combines the advantages genetic algorithms feature with abstractions of immunological processes. Such processes, applied by immune systems trying to protect organisms from biologically and biochemically hazardous entities, intensely increase the learning performance and accuracy of Multi-Layer Perceptrons performing a stochastic search in a space. This is achieved by applying a combination of immunological operations in each population's evolution cycle, which are clonal selection and somatic hypermutation, negative selection and danger theory. Furthermore, causality plays an important role within the introduced paradigm, as the solution population does not only change from generation to generation, but also within each generation. For the immune system-based operations only the individuals of the current generation do matter. Thus, the in-and outputs a population in consideration processes when learning admittedly have a significance over time for the genetic evolution of a single individual (chromosome). However, all of the immune systembased operations do not need to consider these, as only the current population of genomes matters. Currently, only the already introduced, computationally intelligent Data Mining system "System applying High Order Computational Intelligence in Data Mining" (SHOCID) successfully applies the introduced approach for Artificial Neural Network learning.

This paper presents a microcontroller-based project for acquisition of the EKG and EOG signals and transfers to the computer for recording and data analysis. The main purpose of the project is to record both type of signals using the same multichannel device, the instrumentation amplifier and the power amplifier of every channel supports adjustable gain, so can be calibrated. The importance of calibration is due to difference in voltage of the EKG and EOG signals and because every person has different gain of those signals. Analog signal is converted in digital with Atmel microcontroller and transferred to the computer trough serial interface. We developed a PC based software for data recording and signal analysis, resulting the values of the heart rate and the direction of the eye movement