A new immune cultural algorithm(ICA)based on immune clone selection was proposed.In ICA

immune clone machine was used for training and testing sampling data from SRT-Ⅲ furnace.One selection was taken as population space of cultural algorithm.In belief space

the knowledge extraction

expression

storage

update methods were proposed according to their evolutionary characteristics. Communication function was improved at the same time which in turn improved the capacity of algorithm evolution.The test results showed that compared with genetic algorithm(GA)and chemotactic differential evolution algorithm(CDEA)

immune cultural algorithm had much improvement in search precision and convergence speed.The algorithm was applied to the support vector machine parameter optimization for solving fault diagnosis of ethylene cracking furnace.Multi-class classifier was made by support vector machine.Compared with fault classification using the parameters optimized by genetic algorithm

the simulation results showed that the proposed algorithm achieved good result in classification accuracy

20 percentage points higher than the method without using immune cultural algorithms.