Hybrid multiobjective metaheuristics for the design of reliable DNA libraries

DNA-based technologies, such as nanotechnology, DNA sequencing or DNA computing, have grown significantly in recent years. The inherent properties presented in DNA molecules (storage density, parallelism possibilities, energy efficiency, etc) make these particles unique computational elements. However, DNA libraries used for computation have to fulfill strict biochemical properties to avoid undesirable reactions, because those reactions usually lead to incorrect calculations. DNA sequence design is an NP-hard problem which involves several heterogeneous and conflicting biochemical design criteria which may produce some difficulties for traditional optimization methods. In this paper, we propose a multiobjective evolutionary algorithm which is hybridized with a local search heuristics specially designed for the problem. The proposal is a multiobjective variant of the teaching–learning-based optimization algorithm. On the other hand, with the aim of ensuring the performance of our proposal, we have made comparisons with the well-known fast-non dominated sorting genetic algorithm and strength Pareto evolutionary algorithm 2, and other approaches published in the literature. After performing diverse comparisons, we can conclude that our hybrid approach is able to obtain very promising DNA sequences suitable for computation which outperform in reliability other results generated with other existing sequence design techniques published in the literature.