Abstract—DNA computing relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. Therefore, much work has focused on designing the DNA sequences to make the molecular computation more reliable. Sequence design involves with a number of heterogeneous and conflicting design criteria and traditional optimization methods may face difficulties. In this paper, we formulate the DNA sequence design as a multiobjective optimization problem and solve it using a constrained multiobjective evolutionary algorithm (EA). The method is implemented into the DNA sequence design system, NACST/Seq, with a suite of sequence-analysis tools to help choose the best solutions among many alternatives. The performance of NACST/Seq is compared with other sequence design methods, and analyzed on a traveling salesman problem solved by bio-lab experiments. Our experimental results show that the evolutionary sequence design by NACST/Seq outperforms in its reliability the existing sequence design techniques such as conventional EAs, simulated annealing, and specialized heuristic methods. Index Terms—DNA computing, DNA sequence design, multiobjective evolutionary algorithm (MOEA), nucleic acid computing simulation toolkit/sequence generator (NACST/Seq). I.

Previously, direct-proportional length-based DNA computing (DPLB-DNAC) for solving weighted graph problems has been reported. The proposed DPLB-DNAC has been successfully applied to solve the shortest path problem, which is an instance of weighted graph problems. The design and development of DPLB-DNAC is important in order to extend the capability of DNA computing for solving numerical optimization problem. According to DPLB-DNAC, after the initial pool generation, the initial solution is subjected to amplification by polymerase chain reaction and, finally, the output of the computation is visualized by gel electrophoresis. In this paper, however, we give more attention to the initial pool generation of DPLB-DNAC. For this purpose, two kinds of initial pool generation methods, which are generally used for solving weighted graph problems, are evaluated. Those methods are hybridization-ligation and parallel overlap assembly (POA). It is found that for DPLB-DNAC, POA is better than that of the hybridization-ligation method, in terms of population size, generation time, material usage, and efficiency, as supported by the results of actual experiments.

The biological Deoxyribo Nucleic Acid (DNA) strand is found to be a promising computing unit. An attempt has been made to solve symmetric Multiple Travelling Salesperson Problem (MTSP) with single depot using DNA. In this paper, the thermodynamic properties of DNA have been utilized along with other bio-chemical operations to obtain the optimal solution. Actual distance values are possible to be represented using the thermodynamic properties of DNA. Moreover, the proposed approach can be adopted in solving more real-life applications like Vehicle Routing problems and Scheduling problems, with necessary modifications. In this work, an instance with seven cities and three salespersons is solved using DNA computing. This method exhibits the ability to solve NP-complete problems using molecular computing.