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Computational discovery of DNA motifs is one of the major challenges in bioinformatics, which helps in understanding the mechanism of gene regulation. It has been reported that computational approaches have good potential for problem solving in terms of cost and time saving. Based on our previous studies, this paper aims to develop an evolutionary computation scheme to provide an alternative approach for motif discovery. To work on the framework of our previously developed GAPK, a small sized collection of k-mers is extracted and utilized as “prior knowledge” in algorithm development. Our technical contributions in this paper mainly include a novel fitness function carrying information on conservation and rareness of DNA motifs, and a path to access GAPK-like solutions using seed concept and filtering techniques. The proposed algorithm in this paper has been evaluated by using eight benchmarked datasets, with comparisons to well-known tools such as MEME, MDScan, AlignACE and two GA-based techniques. Results show that our proposed method favorably outperforms other algorithms for these testing datasets.