| Ciliate is a type of single-celled eukaryotes, which has cilia or cilia device at least a period of time in the life cycle. It is widely distributed. The research on ciliates eukaryotic origin and evolution does good for aquatic ecosystems and species diversity of the research, water pollution detection and treatment, prevention and treatment of fish diseases and fish production, as well as the feeding of ruminants such as areas of great significance.As complex life history and rich diversity of the ciliates, there has always been controversial on the phylogenetic relationship between ciliates, and it directly affects their basic theoretical research and practical application of the active role. With the molecular biology techniques and computer data processing technology, the rapid development of the discipline in the study of the various bio-genetic relationship between the origin and evolution, etc. has been widely used. Discussion from the molecular level phylogeny of ciliates lays the foundation for Molecular Phylogeny of Ciliates, and so brings about the Molecular Phylogeny .Construction of phylogenetic tree is a common means on phylogenetic analysis, and there are many methods such as Hsp70 (heat-shock protein of 70 ku), histone H4, DNA polymeraseα, as well as the rRNA genes. I use the rRNA genes, because rRNA molecules not only has a conservative structure, but also a high degree of sequence polymorphism, that is to say , mutation of nucleic acid sequences can generate a huge amount of the sequence, the structure is not identical with the the source elements, and the record of biological evolution process, suitable for study of interspecific relationships, choosing to ciliates rRNA phylogenetic analysis. Although there are many difficulties and doubts on ciliate phylogeny, more than 10 kinds of genes for the establishment of molecular phylogenetic tree have been found, and some of them are mature, so is Genetic algorithm.There are many questions on which you need seek the best answers in seeking space in the Artificial Intelligence. When you do this, you should use the questions originally has. Make sure to reduce the seeking space so as to look for the best answers, which is a special subject. Genetic algorithm is one important method, and its calculation is started from a set of possible solution, and it is called populations (population), represented as gene in the genetic algorithm. The fitness function is used to measure the solutions, so the initial number of populations is changed into a higher degree of the solution as new species, and the process is an iterative loop in the algorithm until it reaches the end of the process, including finding the optimal solution, or the program default values, or the number of iterations to reach. The fitness selection runs through iteration in the genetic algorithm, and the solution is optimized gradually.As the input data is encoded data in the genetic algorithm, while the ciliate rRNA gene sequence itself is a sequence of data. So using computer programming to solve biological problems is entirely achievable through modeling and simulation. However, the shortcomings of the genetic algorithm itself, such as premature convergence or getting a local optimal solution and so on, makes it impossible to solve the problem high requirement, so I joined the immune part in the traditional genetic algorithm, and proposed a new immune genetic algorithm.Immune algorithm is an algorithm based on immune theory. Basic immune algorithm is based on the basic mechanisms of biological immune system, mimicking the body's immune system. Algorithm from a somatic cell theory and network theory inspired to achieve a similar biological immune system antigen recognition, cell differentiation, memory and self-regulating functions. By adding the immune genetic algorithm operator, we can improve the efficiency of the algorithm to avoid premature convergence of genetic algorithms and get local optimal solution.In this paper, the global search capabilities of the genetic algorithm is retained, at the same time, I added the immune operator, and the biological systems in the immune response, antigen memory, vaccination, immune selection mechanisms used in traditional genetic algorithm. By programming and testing, the premature convergence has the obvious improvement, and the search capability and efficiency are improved, and we can get the satisfactory results.In the program, the distance-based method of phylogenetic tree construction is used. First, I get ciliate rRNA sequence data source, through the format conversion to computer input and genetic algorithm coding required, I use c++ object-oriented programming to solve the main problem, and Guided by the traditional Immune Genetic Algorithm, I design my own immune operator immunization process, and complete the immunization section. The program code is effective and clear, through continuous operation, I can confirm the feasibility and efficiency of the algorithm.From the knowledge of bioinformatics, Papers introduced on the ciliate phylogenetic analysis with biological knowledge related to a detailed exposition, through data collection, data structures biological knowledge in the handling of the appropriate structure for processing, changed into computer data input to the genetic algorithm, and in the preparation of algorithm to solve the problem step by step process to explore, practice, testing the immune genetic algorithm to complete the improvements and a brief analysis of algorithm complexity, through the program ciliate rRNA gene sequence of the input, comparison, the establishment of the phylogenetic tree model and results analysis shows that numerous experiments have examined the papers of the immune genetic algorithm improved the usefulness and efficiency, on the other hand, the actual data test through the algorithm, provides a more effective way and means to rRNA phylogeny of ciliates analysis of the study. This paper studies the structure of data, use, and programming and testing have shown that the selected data structure and use of reasonable, for the relevant type of data-processing approaches to providing a reliable basis for validation.In order to in-depth study of algorithms, do the next step more in-depth research. Upon completion of algorithms to improve the same time, the advantages and disadvantages compared to similar algorithms, and future directions for improvement. The results showed that this new type of immune genetic algorithm in ciliate rRNA phylogenetic analysis of very effective, and look forward to this algorithm to further promote and improve , to help the relevant aspects of bioinformatics research. Finally I make a brief analysis of the complexity.Practice has proved that the improved immune genetic algorithm in ciliate rRNA phylogenetic analysis of the project is feasible and valuable. The algorithm can be applied to other applications, and it provides a train of thought and means on the combinatorial optimization problems. |
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