本文選題：蛋白質(zhì)折疊 + 遺傳算法　； 參考：《武漢科技大學(xué)》2012年碩士論文

【摘要】：蛋白質(zhì)折疊結(jié)構(gòu)預(yù)測問題是當(dāng)前生物學(xué)研究的一個熱點。由于其特殊的結(jié)構(gòu)和所使用的模型限制，利用NP問題的求解來求其最小能量值從而推測出折疊結(jié)構(gòu)是這個問題的研究方向之一。許多算法用來解決這個問題，然而由于復(fù)雜的計算導(dǎo)致的時間的消耗代價昂貴。遺傳退火算法（GAA）是遺傳算法和模擬退火算法的結(jié)合，同時具備了遺傳算法全局搜索能力強(qiáng)和退火算法局部收斂快的優(yōu)勢，因此成為蛋白質(zhì)折疊結(jié)構(gòu)預(yù)測問題常用算法。 網(wǎng)格計算是一種分布式并行計算，它致力于利用網(wǎng)絡(luò)上的閑置資源來解決大規(guī)模計算問題。網(wǎng)格并行系統(tǒng)的設(shè)計以網(wǎng)格中間件為基礎(chǔ)，具備管理性強(qiáng)、安全性高、數(shù)據(jù)傳輸方便和擴(kuò)展性好的特點，從計算的效率、設(shè)備的代價來看，適合大規(guī)模計算問題的普遍研究。基于MPI的網(wǎng)格編程接口是實現(xiàn)網(wǎng)格并行計算的一種應(yīng)用。 本文利用遺傳退火算法來解決蛋白質(zhì)折疊結(jié)構(gòu)預(yù)測問題，將串行算法移植到并行的網(wǎng)格平臺中。在并行算法中，種群被分為多個子種群分布到子節(jié)點中各自進(jìn)行演化操作。同時根據(jù)實際情況對算法的算子（選擇、交叉、變異）進(jìn)行改進(jìn)，，以求得更好的計算速度和算法效率。
[Abstract]:Protein folding structure prediction is a hot topic in current biological research. Because of its special structure and the limitation of the model used, the solution of NP problem is used to calculate its minimum energy value, and it is inferred that folding structure is one of the research directions of this problem. Many algorithms are used to solve this problem, but the time consumption due to complex computation is expensive. Genetic annealing algorithm (GA) is a combination of genetic algorithm and simulated annealing algorithm. It has the advantages of strong global search ability and fast local convergence of annealing algorithm, so it has become a common algorithm for protein folding structure prediction. Grid computing is a kind of distributed parallel computing, which is dedicated to solving large-scale computing problems by using idle resources on the network. The design of grid parallel system is based on grid middleware, which has the characteristics of strong management, high security, convenient data transmission and good expansibility. From the point of view of computing efficiency and equipment cost, it is suitable for the general research of large-scale computing problems. Grid programming interface based on MPI is an application to realize grid parallel computing. In this paper, the genetic annealing algorithm is used to solve the protein folding structure prediction problem, and the serial algorithm is transplanted to the parallel grid platform. In the parallel algorithm, the population is divided into multiple subpopulations and distributed to the child nodes to perform evolutionary operations. At the same time, the operators (selection, crossover, mutation) of the algorithm are improved according to the actual situation, in order to obtain better calculation speed and algorithm efficiency.
【學(xué)位授予單位】：武漢科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：Q51;TP338.6


本文編號：1894264