The Research On Bio-Inspired Encoding Algorithms For DNA Computing

DNA computing is a novel computation paradigm with DNA molecules and enzymes as"carrier", and biochemistry trials as"information processing instruments". In recent years, DNA computing has been extensively researched in recent years. Since information is encoded in DNA sequences, the design of DNA sequences is crucial for successful DNA computation. DNA sequence design need meet simultaneously several physical, chemical and logical constraints, which is difficult to be solved by the traditional optimization methods. Therefore, this thesis focus on DNA sequences design based on various Bio-inspired algorithm to make the molecular computation more reliable. The main research works are as follows:The first part details the background, the aim and the meaning of study, surveys related domestic and foreign researches, proposes the main work of our study, and interprets the DNA encoding problem.A novel algorithm based on membrane computing for DNA sequences design is proposed. Membrane computing, also called P systems, is a branch of natural computing, which is a class of distributed non-deterministic parallel computing devices of a biochemical type. Membrane computing has been used to solve various optimization hard problems, but it hasn't been employed so far in DNA encoding. In the paper, the new membrane system is constructed, and is firstly used to solve the DNA sequence optimization problem. By using the novel algorithm, a set of good DNA sequences are generated. Furthermore, the simulation results show the efficiency of our method.A novel quantum chaotic swarm evolutionary algorithm (QCSEA) is presented, and is firstly used to solve the DNA sequence optimization problem. Quantum evolutionary algorithm is a novel algorithm based on the quantum computation and evolutionary computation, and has been extensively researched in the recent years. In the paper, a new definition of Q-bit expression called quantum angle is introduced, and the particle swarm optimization is employed to update the quantum angles automatically. By merging the particle swarm optimization and the chaotic search, the hybrid algorithm can not only avoid the disadvantage of easily getting into the local optional solution in the later evolution period, but also keep the rapid convergence performance. And the simulation results demonstrate that the proposed quantum chaotic swarm evolutionary algorithm is valid and outperforms the genetic algorithm and conventional evolutionary algorithm for DNA encoding.DNA sequence design relates to a number of design criteria, which is difficult to select the proper weight values for each vriterion by the weight methods. In this paper, the carrier chaotic searching was merged into multi-objective evolutionary algorithm, and a multi-objective carrier chaotic evolutionary algorithm (MCCEA) for designing DNA sequences was developed. In each generation of MCCEA algorithm, carrier chaotic search is performed on the copy of several individuals chosen randomly from the external archive to obtain new non-dominated solutions. The more non-dominated solutions will be produced by ergodic regularity of chaos. Compared with the traditional algorithm, such as PSO algorithm, GA, weight methods, and so on, our algorithm not only avoids the difficulty of selecting the proper weight values for each criterion, but also escapes from local optimal solution. The simulation results show that the comprehensive performance of multi-objective carrier chaotic evolutionary algorithm is improved by merging chaos in MOEA algorithm.The application of DNA sequecse design is discussed in this paper too. The DNA computation based on Giant Magnetoresistance (GMR) Effect Chip is proposed. Compared with traditional gene assay technology, GMR chip has the simple construction, label-free feature, time-saving detection speed, and conveniently processed information. In the paper, based on the GMR chip and DNA computing theory, a new DNA computing method to solve satisfiability problem is proposed. The DNA sequences of a computational example are generated by quantum chaotic swarm evolutionary algorithm. By using the Primer Premier 5.0, these DNA sequences are tesitified that they are good for avoiding the non-specific hybridization.