Improving the design of sequences for DNA computing: A multiobjective evolutionary approach |
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Authors: | Victor M. Cervantes-Salido Oswaldo Jaime Carlos A. Brizuela Israel M. Martínez-Pérez |
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Affiliation: | Computer Sciences Department, CICESE Research Center, Carretera Ensenada-Tijuana No. 3918, Zona Playitas, C.P. 22860, Ensenada, B.C., Mexico |
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Abstract: | Designing oligonucleotide strands that selectively hybridize to reduce undesired reactions is a critical step for successful DNA computing. To accomplish this, DNA molecules must be restricted to a wide window of thermodynamical and logical conditions, which in turn facilitate and control the algorithmic processes implemented by chemical reactions. In this paper, we propose a multiobjective evolutionary algorithm for DNA sequence design that, unlike preceding evolutionary approaches, uses a matrix-based chromosome as encoding strategy. Computational results show that a matrix-based GA along with its specific genetic operators may improve the performance for DNA sequence optimization compared to previous methods. |
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Keywords: | DNA computing DNA sequence design Multiobjective optimization NSGA-II Genetic algorithms |
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