The immune programming algorithm, inspired by the immune system of human and other mammals, was used to optimize the parameters of weighted support vector machines.
其中根据各样本重要性的不同，引入了加权支持向量机方法，然后利用免疫规划算法对其进行参数优化。
The analysis of reasons and merits for improved part were proposed. The main steps of the algorithm were given and the convergence of adaptive immune evolutionary programming were formulated.
对各部分改进的原因和优点进行了分析，给出了算法的主要步骤，并对自适应免疫进化规划的收敛性进行了说明。
Introduce main problems of Constained Optimization and methods to solve them. Using immune computing algorithm for solving nonlinear programming problem.
介绍了约束条件的函数优化的主要问题及处理约束的解决方法，针对一般性非线性规划问题，采用免疫算法来进行处理。
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