Quantum Clonal Evolutionary Algorithms

The 20th century is a resplendent age with an all-time development of technology and civilization. Matter, energy and communication is three key topics. Computers centered Modern Information Processing and digital information characterized Modern Information Transmission are in process of their close combination. As an active research direction of intelligent information processing, computational intelligence (CI) has attracted many scientists' attention. In recent years, CI is generally considered as a new computational method based on the full development of its three branches-Neural Network(NN), Fuzzy System (FS) and Evolutionary Computation (EC). In fact, CI includes abundant implications. Over a long period, the worldwide researchers are going in different directions and using various methods to approach the essence of CI. Naturally, CI is an abstract subject spanning Physics, Mathematics, Computer Science, Communication, Physiology, Evolution and Psychology. Accordingly, using the extracted knowledge of these subjects can make a deeper investigation into CI and give a basis for the optimization, also help to build up a more uniformly intelligent method of system design. In this paper, a frame of quantum clonal evolutionary algorithm is presented by the combining of quantum theory with the clonal selection theory in artificial immune system (AIS). Its algorithms are given in detail and their convergences are proved. Both the theory analysis and simulations prove its superiority. The content of this paper includes: The clonal selection theory in AIS and the application of the clone operator are present in detail in this paper. Based on the analysis of the bottleneck of the evolutionary algorithm, the quantum clonal evolutionary algorithm is presented. It includes three parts: the quantum clonal genetic algorithms, the quantum clonal evolutionary strategies and the quantum clonal evolutionary programming.