An Efficient Design for a Multi-objective Evolutionary Algorithm to Generate DNA Libraries Suitable for Computation.

The design of reliable DNA libraries that can be used for bio-molecular computing involves several heterogeneous conflicting design criteria that traditional optimization approaches do not fit properly. As it is well known, evolutionary algorithms are very appropriate for solving complex NP-hard optimization problems. However, these approaches take significant computational resources when large instances of complex problems are managed. This is the case for the design of DNA libraries suitable for computation, which involves a set of conflicting design criteria that have to be simultaneously optimized. The problem tackled in this paper involves four objectives and two constraints which are managed at the same time by a tested multi-objective evolutionary algorithm (MOEA) with thousands of individuals in the population. In this context, every computational approach would take several hours of execution time to generate high-quality DNA strands. In this paper, we present an analysis of the parallel MOEA which has been efficiently parallelized with the aim of generating reliable sets of DNA sequences. The results obtained in the study presented here show that the parallel approach is computationally very efficient and that the DNA libraries are highly reliable for computation.

DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm.

In this paper, we use an adapted multi-objective version of the differential evolution (DE) metaheuristics for the design and generation of reliable DNA libraries that can be used for computation. DNA sequence design is a very relevant task in many recent research fields, e.g. nanotechnology or DNA computing. Specifically, DNA computing is a new computational model which uses DNA molecules as information storage and their possible biological interactions as processing operators. Therefore, the possible reactions and interactions among molecules must be strictly controlled to prevent incorrect computations. The design of reliable DNA libraries for bio-molecular computing is an NP-hard combinatorial problem which involves many heterogeneous and conflicting design criteria. For this reason, we modelled DNA sequence design as a multiobjective optimization problem and we solved it by using an adapted multi-objective version of DE metaheuristics. Seven different bio-chemical design criteria have been simultaneously considered to obtain high quality DNA sequences which are suitable for molecular computing. Furthermore, we have developed the multiobjective standard fast non-dominated sorting genetic algorithm (NSGA-II) in order to perform a formal comparative study by using multi-objective indicators. Additionally, we have also compared our results with other relevant results published in the literature. We conclude that our proposal is a promising approach which is able to generate reliable real-world DNA sequences that significantly improve other DNA libraries previously published in the literature.