Artificial Immune System: Principle, Models, Analysis and Perspectives

Drawing inspiration from the vertebrate immune system, a new research field of Artificial Immune System (AIS) is springing up. As a novel branch of computational intelligence, AIS has strong capabilities of pattern recognition, learning and associative memory, hence it is natural to view AIS as a powerful information processing and problem-solving paradigm in both the scientific and engineering fields. This paper intends to give a comprehensive overview of AIS based on a preliminary theoretical framework, which is started with the brief interpretative introduction of biological models of vertebrate immune system, then followed with some extracted bionic principles, viz. immune recognition, immune learning, immune memory, clone selection, diversity generation and maintenance etc. The mapping from natural immune system to AIS models is emphasized in this paper. As a result, some typical AIS based models and algorithms are discussed through classifications. It is the real engineering applications that draw the broad attention of computer scientists to recognize the great potential of AIS, hereby some important application fields as information security, pattern recognition, optimization, machine learning, data mining, robotics, diagnostics and cybernetics etc. are reviewed. Then based on the property analysis of AIS, some key problems in the state-of-the-art of AIS research are investigated, through which we hope to gain deep insight into AIS and suggest some new ideas that may be of value for AIS model development. Finally, some possible research directions of AIS are given by the authors in a further step as the summary of this paper, among which the application of AIS to evolutionary design is emphasized.