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The artificial immune system (AIS) is an emerging research field of computational intelligence that is inspired by the principle of biological immune systems.With the adaptive learning ability and a self-organization and robustness nature, the immunology based AIS algorithms have successfully been applied to solve many engineering problems in recent years, such as computer network security analysis, fault detection, and data mining.The real-valued negative selection algorithm (RNSA) is a computational model of the self/non-self discrimination process performed by the T-cells in natural immune systems. In this research, three different real-valued negative selection algorithms (i.e., the detectors with fixed radius, the V-detector with variable radius, and the proliferating detectors) are studied and their applications in data classification and bioinformatics are investigated. A comprehensive study on various parameters that are related with the performance of RNSA, such as the dimensionality of input vectors, the estimation of detector coverage, and most importantly the selection of an appropriate distance metric, is conducted and the figure of merit (FOM) of each algorithm is evaluated using real-world v datasets. As a comparison, a model based on artificial neural network is also included to further demonstrate the effectiveness and advantages of RNSA for specific applications. vi ACKNOWLEDGMENTS

-Bioinformatics is a data-intensive field of researchIn a negative selection algorithm, detectors are generated and development. The purpose of bioinformatics data mining is randomly first, then they are evolved (i.e., eliminated if they to discover the relationships and patterns in large databases to match any "self" samples) to obtain a set of trained "mature" provide useful information for biomedical analysis and diagnosis.detectors. In the testing mode, each unknown data instance is In this research, algorithms based on artificial immunepresented to the detector set and classified as either "self" or systems (AIS) and artificial neural networks (ANN) are employed "non-self". That is, if the unknown data instance matches any for bioinformatics data mining. Three different variations of the detector in the detector set, then it is classified as "non-self" or real-valued negative selection algorithm and a multi-layer feedforward neural network model are discussed, tested and an anomaly; while on the other hand, if the incoming data compared via computer simulations. It is shown that the ANN instance is not recognized by any detector, it is considered to model yields the best overall result while the AIS algorithm is be a member of the "self" set.