多目标优化机制下DNA编码序列模型

针对现有DNA计算中存在的编码序列设计稳定性、可靠性不完善等问题,充分考虑基本编码问题,设计出一种基于多目标优化机制的DNA编码序列设计算法。在一定的约束条件下,该算法利用了多目标优化机制以及采取小种蚁群算法,将h-distance因子添加到单链DNA架构中,建立一种DNA序列公用方法。通过模拟实验表明,该算法与同类型算法相比,在计算效率、优化性方面具有一定优势。     

基于文化进化粒子群算法的DNA序列设计

DNA编码序列的设计是影响DNA计算可靠性的重要途径,从DNA编码应满足的多约束条件中选取适当的约束条件,针对这些约束条件提出每个DNA个体应满足的评估公式以及目标序列集合的评价函数,采用文化进化粒子群算法解决DNA序列设计的多目标优化问题,仿真结果表明该混合算法针对DNA序列设计问题,在求解最优值能力,解的稳定性方面都取得了不错的效果。     

DNA计算中编码序列的过滤函数研究

构造了用于DNA编码序列过滤的函数,并给出了DNA序列编码的算法,采用该文设计的过滤函数和算法所得到的DNA编码序列,能够满足一定的组合约束条件,并满足一定热力学条件,大大提高了DNA编码字的质量,有利于提高DNA计算的可靠性。     

基于聚类小生境遗传算法的DNA编码优化

DNA编码优化问题是DNA计算中的核心问题。分析DNA编码优化的约束条件,在单链DNA序列集合上引入h距离,将聚类小生境技术应用于小种群遗传算法的构造,对DNA编码优化问题进行求解。基于h距离定义DNA序列间的相似函数,将碱基字母编码为4进制整数、DNA编码序列作为个体编码为4进制整数向量、种群编码为4进制整数矩阵,基于模4算术运算,构造相应的遗传算子,并给出DNA编码序列的具体计算结果。实验结果表明,与现有DNA编码序列优化结果相比,该算法可得到更好的DNA编码序列且计算效率较高。     

基于遗传粒子群算法的DNA编码优化

DNA编码序列的设计是影响DNA计算可靠性的重要手段,该文从DNA编码设计应满足的多约束条件中选取适当的约束条件,针对这些约束条件提出每个DNA个体应满足的评估公式,采用遗传粒子群算法解决该多目标优化问题,并在不同的约束准则下将计算得到的序列与已有的DNA序列进行了对比。仿真结果证明了该方法的有效性。     

基于纠错码编码理论的DNA编码研究*

作为一种新的计算模式,DNA计算有着强大的计算能力,编码问题在DNA计算中占据重要的位置,有效的编码设计能够提高DNA计算的可靠性。基于纠错码编码理论,提出了一种新的DNA编码方法,该方法可以找出具有一定长度且满足汉明距离约束的DNA编码序列。最后,给出了该算法的仿真,结果表明了该算法的有效性。     

基于改进非支配遗传算法的DNA编码序列优化方法

针对DNA计算中的编码序列设计问题,分析了DNA编码序列设计的目标和需要满足的约束条件,并建立了相应的数学模型。通过将约束条件引入非支配排序过程,提出了一种改进的NSGA-Ⅱ算法。实验结果表明,该算法具有良好的收敛特性和种群多样性,能为可控的DNA计算提供可靠的编码序列。     

基于改进的粒子群遗传算法的DNA编码序列优化

在DNA计算中,DNA编码序列的设计是影响DNA计算可靠性的重要手段.在不同的DNA序列设计中,应该选择适当的约束条件,并且根据相应的约束条件提出每个DNA应该相应满足的评估公式.文中从DNA编码设计应满足的多约束条件中选取适当的约束条件,提出评估公式,并采用改进的粒子群遗传算法来解决多目标优化问题.同时根据得到的序列与已有序列在综合适应度函数结果上进行对比,结果证明了该方法的有效性.     

一种基于编码等价变换和遗传算法的DNA序列优化设计

针对DNA计算中的DNA序列设计问题,基于6个DNA序列设计约束条件,将DNA序列设计问题转化为多目标优化问题,提出小生境遗传算法进行求解。算法利用DNA序列设计中的相似性约束与H-测度约束,在单链DNA序列集合上定义共享函数,利用两种类型的编码等价变换以及模4算术运算,构造了5个遗传算子,并给出具体的DNA序列设计结果。通过比较,算法可以得到质量更好的DNA序列,且在种群规模与进化代数方面具有更高的计算效率。     

基于文化遗传算法的DNA编码序列设计

DNA编码问题是DNA计算的关键,然而,它已被证明为NP困难问题,通常采用优化算法求解。针对传统遗传算法缺乏有效指导,容易陷入局部极值的缺点,结合文化算法采用种群空间和信念空间的双层进化结构进行寻优,提出了一种基于遗传算法和文化算法的混合优化算法用于解决DNA编码问题。仿真结果表明该混合算法能有效地用于DNA编码序列设计。     

DNA计算中编码序列的优化设计方案*

提出了一种优化设计方案.该方案的各项评价指标均优于根据以往文献提供的方法所能得到的最好结果.尤其是所提出的海明距离测度方法,进一步保证了特异性杂交产生的自由能远大于非特异性杂交所产生的自由能,便于进行DNA编码序列的设计与选择,为可控的DNA计算提供可靠有效的编码序列.     

Application of a novel IWO to the design of encoding sequences for DNA computing

Encoding and processing information in DNA-, RNA- and other biomolecule-based devices is an important requirement for DNA based computing with potentially important applications. To make DNA computing more reliable, much work has focused on designing the good DNA sequences. However, this is a bothersome task as encoding problem is an NP problem. In this paper, a new methodology based on the IWO algorithm is developed to optimize encoding sequences. Firstly, the mathematics models of constrained objective optimization design for encoding problems based on the thermodynamic criteria are set up. Then, a modified IWO method is developed by defining the colonizing behavior of weeds to overcome the obstacles of the original IWO algorithm, which cannot be applied to discrete problems directly. The experimental results show that the proposed method is effective and convenient for the user to design and select effective DNA sequences in silicon for controllable DNA computing.     

最小自由能约束的DNA编码设计研究

首先介绍了DNA编码设计中自由能约束的重要性,以及自由能约束的计算公式,进而采用一种改进的蚁群优化算法来求解。仿真实验表明此算法产生一组能满足特定自由能约束和统一的解链温度约束的DNA序列,算法利用蚁群算法的并行性提高了编码设计算法的效率,利用最小自由能约束产生更稳定的DNA序列。     

基于神经网络预测的SNP信息的剪接点识别算法研究

随着基因组计划的完成,人们需要尽快从这些海量数据中了解基因组的结构,揭示生命的奥秘,剪接位点识别是其中的一个重要环节,然而到目前为止该问题仍未能得到很好的解决。在分析此问题时引入了第三代遗传标记单核苷酸多态性(SNP),以期探索变异对剪接机制的影响;其次,对DNA序列的数字化进行了探讨。通过实验表明,单核苷酸多态性的引入对于剪接位点识别算法的性能有着一定的影响,此外文中提出的编码方法对预测精度的提升亦有正面作用,整体效果比目前常用方法有了大幅提升。     

A set of Macintosh computer programs for the design and analysis of synthetic genes

Computer programs that can be used for the design of synthetic genes and that are run on an Apple Macintosh computer are described. These programs determine nucleic acid sequences encoding amino acid sequences. They select DNA sequences based on codon usage as specified by the user, and determine the placement of base changes that can be used to create restriction enzyme sites without altering the amino acid sequence. A new algorithm for finding restriction sites by translating the restriction endonuclease target sequence in all three reading frames and then searching the given peptide or protein amino acid sequence with these short restriction enzyme peptide sequences is described. Examples are given for the creation of synthetic DNA sequences for the bovine prethrombin-2 and ribonuclease A genes.     

A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design

The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for bio-molecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published.     

A simple and fast DNA compressor

In this paper we consider the problem of DNA compression. It is well known that one of the main features of DNA sequences is that they contain substrings which are duplicated except for a few random mutations. For this reason most DNA compressors work by searching and encoding approximate repeats. We depart from this strategy by searching and encoding only exact repeats. However, we use an encoding designed to take advantage of the possible presence of approximate repeats. Our approach leads to an algorithm which is an order of magnitude faster than any other algorithm and achieves a compression ratio very close to the best DNA compressors. Another important feature of our algorithm is its small space occupancy which makes it possible to compress sequences hundreds of megabytes long, well beyond the range of any previous DNA compressor. Copyright © 2004 John Wiley & Sons, Ltd.     

基于文化微粒群优化算法的DNA编码研究

对DNA编码约束进行研究,选择汉明测量以及相似度作为DNA序列集设计的主要约束,并结合连续性约束与GC Content约束,将序列集设计问题抽象为带有强约束的多目标优化问题,采用文化微粒群算法解决该多目标优化问题。仿真结果表明,该混合算法针对DNA编码序列设计问题,在求解最优值能力、解的稳定性方面都能取得较好的效果。     

DNA编码序列设计的混合进化算法优化

分析编码序列设计的目标及需要满足的约束条件,建立相应的数学模型,提出该模型的模拟退火遗传优化算法(HSAGA).模拟退火采用串行优化结构,遗传算法采用群体并行搜索,两者结合成为并行算法.模拟退火作为一种自适应变概率的变异操作,可有效增强并补充遗传算法的进化能力.通过具体算法的实现,得出较高质量的DNA编码序列.