Improving the design of sequences for DNA computing: A multiobjective evolutionary approach

•We propose a multiobjective evolutionary algorithm for DNA sequence design.•The algorithm uses a matrix-based chromosome as encoding strategy.•The algorithm uses specific recombination operators.•We show that our approach may improve the performance for this problem compared to previous methods.

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.