Abstract ;An adaptive immune algorithm, based on the functions of the biological immune system such as adaptive learning, memory and surveillance, is proposed to solve high dimensional dynamical function optimization. In the algorithm, with structural simplicity, feasibility, and dynamical regulation of the execution time for different environments, the dynamical evolution and antibody rearrangement are involved. The dynamical memory pool consists of memory subsets related to the characteristics of immune memory and dynamical maintenance of the pool, in which each subset keeps some excellent memory cells obtained by the average linkage. In the meanwhile, dynamical surveillance and memory establish the environmental identification and generation rule of initial antibody populations. Experimental results and comparison illustrate the superiority and the effective tradeoff between performance effect and efficiency as well as the potential in the complex dynamical highdimensional optimization problems.
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2007, Vol. 20 
