免疫算法研究

abstract：From 1950's, artificial neural network and genetic algorithm has been proposed based on the simulation of biological system. Both of the methods have been sucessfully and extensively used in various fields. Immune algorithm is another method in evolutionary computation, which is proposed by simulating the biological process of an immune system. In biological immune system, there is not only the recognition among the involved cells, there is communication from one to another but also. As a consiquent, immune system has functions of information processing. Therefore, it is possible to propose novel and efficient algorithms based on the simulation of the immune mechanism by using mathematical and computer techniques. In this project, studies on immune algorithm in chemistry field is proposed, and several new algorithms, e.g., immune algorithm, immune - genetic algorithm, immune factor analysis and fast annealing evolutionary algorithm, are established. Applications of these methods in analytical chemistry signal processing for complex multicomponent analysis and optimization of complex chemical problems are also studied. It is found that, by using the proposed signal processing methods, the multicomponent overlapping chromatogram or spectrum can be resolved, and the resolved results can be used for subsequent qualitative and quantatative analysis. By using the proposed optimization methods, new results can be obtained when they are used in the structral optimization of clusters such Lennard-Jones cluster and C60 cluster. These results can provide new methods or tools for analytical signal processing and molecular simulation studies.

首次提出了基于生物体系中免疫系统的计算机模拟在化学领域中建立免疫算法、免疫网络模型、以及人工生命算法。结合化学信号的特点，开发具有高效的识别能力、搜索能力、自适应能力、学习能力并适用于化学信号处理的新型计算方法。本项目具有一定的创新性和实用性，对促进计算机化学领域的发展，特别是化学信息的智能处理具有重要意义。