Immune engineering for Elgasir algorithm optimization

Fuzzy regression trees are a generalization of the standard artificial intelligence technique of regression trees. The Elgasir algorithm has previously been used to create fuzzy regression trees in order to improve the performance of crisp regression trees. A weakness of this approach was that no optimisation of tree node membership functions took place. Artificial Immune Systems are an evolutionary methodology, inspired by the principles and processes of the natural immune system. In this paper a novel method based on an optimization version of Artificial Immune Network model (opt-aiNet) is used to optimize the Elgasir algorithm. In order to illustrate the prediction accuracy of the proposed method, two problem datasets from the UCI repository are used to evaluate the approach. Experimental results have shown the effectiveness of using opt-aiNet for optimization Elgasir algorithm by increasing the prediction accuracy and robustness of fuzzy regression trees.