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In biological sequence analysis, motif finding for the identification of functional regulatory segments underlying gene expression remains a challenge. Recently, we have developed an immune genetic algorithm for motif finding named IGAMD, which adopts vaccine and concentration regulation mechanisms. This paper aims to further improve the accuracy and efficiency of our previous motif finder. There are mainly two fundamental contributions in this work. First, we improve the immune genetic algorithm by adopting an immune network model. The newly proposed algorithm is crossover-free and applies somatic hypermutation proportionally to the fitness of antibody. Concentration regulation mechanism is associated with cloning rate, leading the population size to be dynamically adjustable. A local search operator is also employed to maintain the local optima. Second, we incorporate directed information (e.g. bioinformative position priors and computational seeds obtained from preprocessing by existed tools) when prior knowledge is available, which is beneficial for achieving better performances by reducing the search space. The experimental results indicate that the new approach favorably outperforms IGAMD on the testing data sets.