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Immunological computation is one of the largest recent bio-inspired approaches of artificial intelligence. Artificial immune systems (AIS) are inspired by the processes of the biological immune systems like the learning and memory characteristics which are used for solving complex problems. During the last two decades, AIS have been applied in various fields such as optimization, network security and data mining. In this article, we focus on the application of AIS to data mining in bioinformatics, more specifically, the classification task. For this purpose, we suggest three immune models based on clonal selection theory for the identification of G-protein coupled receptors (GPCRs) to predict their function. Our three classifiers are the artificial immune recognition system (AIRS), the clonal selection algorithm (CLONALG) and the clonal selection classification algorithm (CSCA). The GPCRs represent one of the largest and most important families of multifunctional proteins and are a significant target for bioactive and drug discovery programs. It is estimated that more than half of the drugs on the market currently target GPCRs. However, although thousands of GPCRs sequences are known, many of them remain orphans, have unknown function. Our experiments show that the three immunological classifiers have provided interesting results, however, AIRS obtained the best ones. Therefore, it is, for us, the most suitable immune model for the GPCRs identification problem.
W artykule przedstawiono porównanie algorytmu ewolucyjnego i algorytmu selekcji klonalnej w zadaniu doboru grubości ścian korpusu wrzeciennika frezarki pionowej. Obliczenia przeprowadzono dla różnych nastaw parametrów rozpatrywanych algorytmów optymalizacyjnych. W obu przypadkach zastosowano kryteria sztywności statycznej i masy, które posłużyły do sformułowania funkcji oceny zgodnie z metodą ważonego sumowania kryteriów.
The article presents example of use the clonal selection algorithm and evolutionary algorithm to selection of walls thickness of fixed headstock. Described is the way of modelling the headstock in FEM connection. The criterions of stiffness and mass were applied in both algorithms. These criterions were used to compute of evaluated function by weighed sum method. The results of clonal selection and algorithm evolutionary algorithm are presented.
Non-stationary optimization with the immune based algorithms is studied in this paper. The algorithm works with a binary representation of solutions. A set of different types of binary mutation is proposed and experimentally verified. The mutations differ in the way of calculation of the number of bits to be mutated. Obtained results allow to indicate the leading formulas of calculation.
This paper presents a novel approach to data clustering and multiple-class classification problems. The proposed method is based on a metaphor derived from immune systems, the clonal selection paradigm. A novel clonal selection algorithm - Immune K-Means, is proposed. The proposed system is able to cluster real valued data efficiently and correctly, dynamically estimating the number of clusters. In classification problems discrimination among classes is based on the k-nearest neighbor method. Two different types of suppression are proposed. They enable the evolution of different populations of lymphocytes well suited to a given problem : clustering or classification. The first type of suppression enables the lymphocytes to discover the data distribution while the second type of suppression focuses the lymphocytes on the classes' boundaries. Primary results on artificial data and a real-world benchmark dataset (Fisher's Iris Database) as well as a discussion of the parameters of the algorithm are given.
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