Abstract
Sets of DNA strands that satisfy combinatorial constraints play an important role in various approaches to biomolecular computation, nanostructure design, and molecular tagging. The problem of designing such sets of DNA strands, also known as the DNA code design problem, appears to be computationally hard. In this paper, we show how a recently proposed stochastic local search algorithm for DNA code design can be improved by using hybrid, randomised neighbourhoods. This new type of neighbourhoods tructure equally supports small changes to a given candidate set of strands as well as much larger modi.cations, which correspondt o random, long range connections in the search space induced by the standard (1-mutation) neighbourhood. We report several cases in which our algorithm finds wordset s that match or exceedt he best previously known constructions.
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Tulpan, D.C., Hoos, H.H. (2003). Hybrid Randomised Neighbourhoods Improve Stochastic Local Search for DNA Code Design. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_31
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DOI: https://doi.org/10.1007/3-540-44886-1_31
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