An Improved Genetic Algorithm for Design of DNA Sequence Sets
DNA computing is a new computing paradigm that uses biological molecule DNA to deal with some practical computing problems. The design of DNA sequence sets plays an important role in improving the reliability and the scale of DNA computing. To obtain the largest set which satisfies some constraints is the purpose of designing DNA sequence sets. In this paper, we firstly use the improved genetic algorithm to design DNA sequence sets with Hamming distance and reverse Hamming distance combinatorial constraints. By comparing our experimental results with the previous work, our results improve the lower bounds which satisfy combinational constraints, and further shorten the value range of DNA coding bounds. Furthermore, we use this algorithm to design DNA sequence sets with Hamming distance, reverse Hamming distance, reverse-complement Hamming distance and GC content combinatorial constraints. To the best of our knowledge, these results are obtained for the first time. However they are very important to DNA coding. At the same time, we also obtain some exact values by analyzing and comparing the results. All of these results provide direction for the research of theoretical bounds in DNA coding and the bounds of 4-ary in coding theory.
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Document Type: Research Article
Publication date: 2010-06-01
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- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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