Abstract
Based on the clonal selection theory, a novel artificial immune system algorithm - adaptive immune clonal strategy algorithm (AICSA) is proposed in this paper. According to the antibody-antibody affinity and antibody-antigen affinity, the algorithm can allot dynamically the scales of the immune memory unit and antibody population. By using clone selection, the algorithm can combine the local search with the global search. Compared with classical evolutionary strategy (CES) and immunity clonal strategy (ICS), AICSA is shown to be a strategy capable of solving complex machine learning tasks, like numerical optimization problems, and generally, the algorithm is found to be converged in fewer generations and evaluate function value in the less times for the given accuracy. It is proved theoretically that the AICSA is convergent with probability 1.
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